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What’s New at the 2026 DAC Exhibits

What’s New at the 2026 DAC Exhibits
by Daniel Payne on 06-10-2026 at 10:00 am

dac26 barter ad semi 400 01

The most common question that I get each year at DAC is, “So, what’s new?” When I reviewed the exhibitor list I was pleasantly surprised to see how many EDA, IP and AI companies were attending that I didn’t know about. Here’s just a quick preview of what to expect in Long Beach from July 27-29. I’ll be walking the exhibit floor, visiting companies and blogging in more detail, so stay tuned.

Agentic Design Automation where EDA tools are run to pursue goals, interpreting results, making decisions, adjusting, then iterating to meet objectives. GPU acceleration used with reinforcement learning.

They offer power, energy and thermal management tools. EnergyLab LITE does power simulations and run-time measurements for NXP i.MX evaluation boards. EnergyLab PRO uses an IDE for power simulations and run-time measurements of any HW and SW, like Xilinx boards. Seed Power Manager gives embedded software/firmware users the run-time power, energy, latency and thermal numbers.

Instead of point tool acceleration, this company offers an Intent Engine to keep the spec and decisions connected and current, an Execution Engine for all teammates to use across all tools, and a Knowledge Engine that learns from every project to make the next project start quicker.

This firm helps other companies build the AI-native semiconductor stack, with clients like: InPSY, Quaxys, Rise Design Automation, ModelCat, CraftiFAI, softweb solutions, yieldWerx, Glide Systems, Axiomise, Tuple Tech, MachineWare.

Helps to shield your microelectronics from Cyberattacks, with end-to-end microelectronics security from simulation to silicon.

Their PLM is both open and adaptable, used by companies like: Toyota, Renesas. Competes with PLM from Siemens, Dassault and PTC.

Has an AI lab for the compute stack, starting with chip design acceleration for HW/SW co-design.

Checks your PCB details with a more thorough ERC flow. Used by Tomorrow Lab, StarFish Medical, Igor Institute, SGW, Puzzle Medical.

Add security agents for silicon verification so that you can find and fix any security vulnerabilities earlier in the design flow, driven by AI.

If you want to manufacture an electronics product without component shortages, compliance gaps or production delays, then check out what Cofactor has in their electronics supply chain infrastructure.

Prototyping and emulation that spans from a single FPGA card, to enterprise prototyping with up to 128 FPGA environments. Their team has over 30 years experience in this field.

Benefit from specialized IP like: RF Beamformers, RF front-end modules, low-noise LDOs and XOs, and general purpose MCUs.

Receive RF turnkey products in hardware and software or training through AI-enhanced designs for wireless communication, radar and satellite applications. Aiora Artemis is an AI-assisted optimizer for RF EDA tuning and application. Touchstone Viewer Studio lets your engineers view, analyze and customize Touchstone files.

They have AI-driven aerodynamic optimization, photonic IC design, CFD simulation, electromagnetics (RF) simulation, and integrated photonics simulation.

Big claim of using just a five-person design team with agent leverage to produce what a fifty-person design team used to require, starting with spec input and providing tape-out data ready for silicon. Agents for DV regression triage, IP adoption and derivation, PPA design space exploration, Coverage closure.

AI-based tools for silicon designers using full-stack AI agents working with a web or desktop GUI, with an initial focus on analog, RF and custom circuits.

More AI-powered semiconductor design and verification tools, with a roadmap that includes a tapeout assistant, post-silicon debug and VLSI training.

Experience AI-powered decision making systems for your enterprise through GPU-accelerated computing, deep learning and reinforcement learning in the InstaDeep tool. Their technology could benefit fab engineers.

Expect to see AI-acceleration applied to silicon verification, circuit model creation and multi-physics simulation with their Discovery Platform. Analog circuit modeling is their first focus.

Promising to help your IC design team go from spec to tapeout in spite of talent scarcity, complexity and time crunch. Tool roadmap includes: VerifAgent, DebugAgent, CoverageAgent, SpecAgent, SOCVerify.

AI optimization software to speed the design of analog ICs with the Spaceman tool to automated analog block sizing inside an existing EDA flow.

Offering a stack for complex IP and SoC verification, where you need to generate collateral from specs, speeding up the signoff process, finding edge cases that general purpose LLM and engineers miss.

The Oboe FPGA prototyping system  lets you go from RTL to prototype in just minutes, accelerating verification and bug hunting, measure performance on real workloads and make architecture comparisons.

AI acceleration applied to PCB layout, producing results in just hours, not months by using a physics-driven approach to PCB placement and routing.

DRC and LVS for large chips takes a big effort, so Ramtera has a new approach for physical verification that claims to be both fast and accurate.

Quite the pedigree with talent from Google DeepMind, Anthropic, NVIDIA, Cadence, Apple, xAI, Stanford, MIT and Harvard. They are a frontier AI lab working on a self-improving system to speed chip design with $335M in startup money.

This company has AI hardware agents that can design, verify, debug and even document a semiconductor system, with a future promise of collaboration and autonomy.

One of the few startups with customer names like Tenstorrent and SiFive, this company enables frontend digital design teams to build better SoC and IPs faster with Silimate by autonomously finding and fixing critical front-end issues in a design.

With SuiEM you get EM extraction, analysis and optimization tools for complex modeling and simulation. Applications include 5G, RF/wireless systems, 3-D multi-die packaging and assemblies.

An approach to use spec-first agentic AI to turn specifications into RTL and executable verification plans, testbenches, assertions and coverage closure workflow.

Design Conductor is an autonomous agentic system using frontier models to design semiconductors end-to-end, from concept to GDS II.

In development are models and agents that can learn, evaluate, plan, experiment and interact with the physical world. Founders hail from Stanford, SAIL Researchers, Synopsys, Globalfoundries, Cadence.

 

Summary

This may be the largest collection of startup companies that I recall seeing at one DAC, so my time will be spent getting to learn more about some of these companies. In this list we see much AI technology, so let’s see how it gets proven in the field by design and verification teams. I hope to see you at DAC next month in California, and stop me in the halls to say hello, I’ll be carrying an iPad and looking to hear your thoughts on the industry.

Also Read:

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John Barr: The EDA Veteran and Award-Winning Needham Funds Portfolio Manager

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Optimizing Photonic Integrated Circuit Production with yieldHUB Analytics

Optimizing Photonic Integrated Circuit Production with yieldHUB Analytics
by Daniel Nenni on 06-10-2026 at 8:00 am

Photonics yieldHUB 2026

As Photonic Integrated Circuits (PIC) continue to gain momentum across datacom, telecom, AI infrastructure, sensing, and quantum computing applications, the need for advanced manufacturing analytics has become increasingly critical. To address the challenges associated with scaling PIC production while maintaining high levels of quality and reliability, NewPhotonics® has adopted yieldHUB’s analytics platform as a key component of its manufacturing and quality strategy.

PIC manufacturing presents unique complexities compared to traditional semiconductor devices. While leveraging many of the same wafer fabrication processes, photonic devices introduce additional variables related to optical performance, waveguide uniformity, coupling efficiency, material interactions, and packaging tolerances. As production volumes increase, manufacturers require sophisticated data management and analytics capabilities to monitor process variations, identify yield detractors, and accelerate root-cause analysis across the entire manufacturing flow.

yieldHUB’s platform provides NewPhotonics with a centralized environment for collecting, integrating, and analyzing data from multiple manufacturing sources. This includes wafer fabrication, optical and electrical test systems, assembly operations, reliability screening, and final qualification data. By consolidating previously fragmented datasets, engineering teams gain a comprehensive view of device performance throughout the product lifecycle.

One of the primary benefits of the platform is enhanced yield management. PIC devices often involve complex interactions between optical and electronic structures, making traditional yield analysis methods insufficient. yieldHUB enables advanced correlation analysis between process parameters, test measurements, and failure mechanisms. Engineers can rapidly identify process excursions, detect systematic defects, and isolate variables impacting device performance. This data-driven approach significantly reduces the time required to resolve manufacturing issues and improves overall production efficiency.

The adoption of yieldHUB also strengthens NewPhotonics’ reliability engineering capabilities. Reliability qualification for photonic devices requires extensive characterization under a variety of environmental and operational stress conditions, including temperature cycling, high-temperature operating life (HTOL), humidity exposure, and optical power stress testing. Managing and interpreting large volumes of reliability data can be challenging without a robust analytics infrastructure.

Through automated data aggregation and visualization, yieldHUB allows reliability engineers to monitor degradation trends, analyze failure distributions, and identify early indicators of long-term reliability concerns. Statistical tools within the platform support predictive modeling and lifetime analysis, helping NewPhotonics establish stronger confidence in product robustness and field performance. These capabilities are particularly important as PICs are increasingly deployed in mission-critical applications where downtime and performance degradation are unacceptable.

Another significant advantage is improved traceability throughout the manufacturing process. End-to-end traceability enables engineers to connect individual device performance metrics with specific process conditions, equipment settings, material lots, and assembly operations. This level of visibility is essential for maintaining quality standards, supporting customer audits, and meeting the stringent qualification requirements of telecommunications, data center, aerospace, and defense markets.

The platform’s advanced dashboarding and reporting capabilities further enhance decision-making across engineering, operations, and executive teams. Real-time access to key performance indicators allows organizations to proactively manage production health, monitor yield trends, and prioritize continuous improvement initiatives. Rather than reacting to quality issues after they occur, teams can identify emerging risks and implement corrective actions before they impact customers or production schedules.

For the broader photonics industry, NewPhotonics’ adoption of yieldHUB reflects an important trend toward greater manufacturing intelligence and digital transformation. As PIC technologies move from research and development into high-volume production, success increasingly depends on the ability to leverage data as a strategic asset. Advanced analytics platforms provide the foundation for scalable manufacturing, enabling organizations to achieve higher yields, improve reliability, and accelerate time-to-market.

Bottom line: The integration of yieldHUB’s analytics platform positions NewPhotonics to strengthen product quality, enhance manufacturing consistency, and support the growing demand for high-performance photonic solutions. By combining advanced data analytics with rigorous engineering practices, NewPhotonics is building a more resilient manufacturing ecosystem capable of delivering reliable PIC products at commercial scale while maintaining the quality standards required by next-generation optical technologies.

Also Read:

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Podcast EP301: Celebrating 20 Years on Innovation with yieldHUB’s John O’Donnell

SemiWiki Outlook 2025 with yieldHUB Founder & CEO John O’Donnell


Disaggregating AI Compute to Break the Tokens Barrier

Disaggregating AI Compute to Break the Tokens Barrier
by Bernard Murphy on 06-10-2026 at 6:00 am

Before and after with token servers

Among several topics dominating news streams these days, giant datacenters are a leading theme. They point to an AI-centric future while raising real concerns about sustainability and scalability. Certainly land, power and water demand are very present concerns for most of us, witness growing pushback against building new datacenters. At the same time analysts and investors are clear that the datacenter center boom is not sustainable and are trying to guess when and how this growth collapses. It’s worth going a bit deeper than the daily tea leaf readings to better understand what is happening and how it can more realistically evolve. My thanks to the team at Quadric (Steve Roddy, CMO and Daniel Firu, CPO/Co-Founder, who presented this concept at a recent Silicon Catalyst conference) for these insights.

The tokens rollercoaster

In the spirit of driving adoption, and because nobody yet knows how to measure productivity through AI other than by tokens consumed, the AI heavyweights (Anthropic, OpenAI and Gemini) first offered unlimited token usage. This worked spectacularly: usage ramped very fast, spawning the Token Maxxing phenomenon. Hyperscalers committed hundreds of billions of dollars to building giant datacenters. Then of course analysts speculated that they were building ahead of demand and concern grew that a crash was right around the corner.

Well not quite. Turns out that datacenters are already fully utilized and demand still exceeds supply. Anthropic and others have started pushing license holders into new pricing models, increasing costs to throttle demand. Meanwhile they have signed a deal for capacity on Elon Musk’s Colossus at a claimed $1.25B/month, which would match or exceed SpaceX’s Starlink revenues. (According to SpaceX’s SEC Form S-1 filing the company reported consolidated revenue of $18.7 billion for 2025. Anthropic’s rental of Colossus is reported at $16B annually). Token demand is still ahead of supply and our token maxxing days are over. Enterprises are already looking for clarity in ROI and more grounded ways to measure AI productivity.

Equally they want to limit dependency on cloud-hosted AI. As license deals are rewritten, casual office AI users may not see a big impact but engineering users who have been building major productivity advances around AI in their workflows cannot afford to hit a token ceiling mid-morning. They need to run on more flexible “token servers”, on-prem domain-specific AI capacity which can connect to cloud-level token servers only as needed for general-purpose reasoning/RAG/etc.

Disaggregating token serving capacity

Take Anthropic Claude as an example. Great capabilities, wildly popular and it runs in the cloud. It could also run in a local (on-prem) cloud, but the full model is overkill for domain specific purposes. You really want a full-Claude/mini-Claude approach, the mini version a locally hosted slimmed down model with added domain-specific training, tapping a cloud service when required.  Hardware engineers are already building similar systems. The in-house version will be a high-demand service. Many engineers, each launching their own orchestrator, each in turn launching multiple sub-agents. Public cloud services make this work with Kubernetes managing the whole show, running multiple containerized agents.

Replicating public cloud Claude activity would strain enterprise in-house compute budgets, demanding more servers, more networking, more storage. Much better if the enterprise could add more affordable token servers as specialized hardware running their domain-specific mini-Claudes, with flexibility to add more as demand increases.

Affordable implies low cost – under $1000 and modular for wide adoption. We have already seen Qualcomm, Microsoft and others promoting AI PCs. NVIDIA now has their own AI PC offering (introduced June 1st) with anticipated price tags for fully featured RTX Spark-powered PCs as high as $5000. Some engineering teams are now configuring Mac Minis to get even closer to the token server idea (Quadric is already doing this in their shop). The motivation is simple. Local AI platforms from the big guys are seeding this direction, while healthy competition from new system ventures will further expand options.

Great opportunity for an exploding business in pre-packaged token servers. Quadric think so. Slow down building politically fraught giga-datacenters with high token rates and bring the bulk of your AI workload back in-house using dedicated open-source models, or “mini-Claudes” adapted to your needs. Quadric is pushing this message: a token server appliance could cost as little as a commodity laptop does today, with effective tokens/second output at far more affordable levels. Such a server’s silicon platform is mostly NPU horsepower with a small CPU cluster to run tasks that can be managed locally (even on your own desktop). Quadric is already working with clients, building on Quadric NPU IP, to fashion such appliances.

You can learn more HERE.

Also Read:

Podcast EP336: How Quadric is Enabling Dramatic Improvements in Edge AI with Veer Kheterpal

Quadric’s Recent Momentum & Funding Success

2026 Outlook with Steve Roddy of Quadric


Customized Foundation IP Enables the Next Generation of Automotive Compute

Customized Foundation IP Enables the Next Generation of Automotive Compute
by Kalar Rajendiran on 06-09-2026 at 10:00 am

chip design for blog

As vehicles become increasingly software-defined, automotive semiconductor suppliers face growing pressure to deliver higher compute performance while maintaining strict requirements for power efficiency, reliability, and long-term product support. Advanced driver assistance systems (ADAS), electrification, vehicle networking, and intelligent control systems are all driving greater computational demands across automotive platforms.

While advanced process nodes continue to improve performance and density, scaling alone is no longer enough. Automotive designers must balance increasing compute demands against stringent reliability standards, extended product lifecycles, and challenging operating environments. As a result, many companies are exploring how advanced process technologies and application-specific optimization can work together to enable the next generation of automotive compute.

Why FinFET Matters for Automotive SoCs

As automotive compute requirements continue to rise, the limitations of traditional planar technologies have become increasingly apparent. Achieving higher performance often comes with greater power consumption, increased leakage, and diminishing area efficiency.

FinFET technology helps address these challenges through improved transistor control, enabling higher performance, lower leakage, and better power efficiency. These advantages make FinFET an attractive path for automotive semiconductor suppliers seeking to support next-generation workloads while maintaining demanding quality and reliability requirements.

Automotive Reliability Creates Unique Challenges

Despite its benefits, FinFET adoption introduces additional complexity. Automotive devices must support long service lifetimes, operate across broad voltage and temperature ranges, and meet stringent quality expectations with extremely low defect rates.

As a result, transitioning to FinFET is not simply a process migration. Logic libraries, embedded memories, characterization flows, and reliability methodologies must all be validated for automotive-grade operation. Success requires a coordinated approach that addresses technology, IP, characterization, and reliability together.

Synopsys Customized Foundation IP as an Enabler

Many automotive applications require optimization beyond standard IP offerings. Power budgets, leakage targets, operating conditions, and reliability objectives often demand additional customization to meet product-specific requirements.

Building on the proven capabilities of Synopsys Foundation IP, customization of these IP enables logic libraries, embedded memories, and characterization views to be tailored for specific automotive requirements. This approach helps design teams achieve demanding performance and efficiency targets while accelerating adoption of advanced process technologies.

Customer Engagements Illustrate the Value of Customization

Recent customer engagements demonstrate how customized Foundation IP can help automotive semiconductor suppliers navigate advanced-node transitions.

In one collaboration, a leading automotive semiconductor company sought to deploy a next-generation SoC portfolio on an automotive-qualified FinFET process. While standard Synopsys Foundation IP provided a strong starting point, requirements for ultra-low leakage, expanded characterization coverage, and stringent internal reliability targets required additional customization.

Working closely with the customer, the Synopsys Foundation IP team leveraged its proven IP portfolio and extended process, voltage, and temperature characterization to better reflect automotive mission profiles while optimizing memory solutions for low-power operation and reduced leakage. The collaboration also expanded to include enhanced reliability analysis and measurement methodologies, combining the customer’s application expertise with Synopsys IP design and characterization experience.

The resulting solution enabled the customer to advance its FinFET roadmap with confidence while meeting aggressive performance, power, and reliability objectives.

What These Engagements Reveal About Automotive Node Adoption

A broader lesson emerging from these collaborations is that advanced-node adoption in automotive markets is increasingly a system-level challenge.

Success depends not only on transistor technology, but also on aligning IP development, characterization, reliability methodologies, and implementation flows around application-specific requirements. Synopsys customized Foundation IP serves as a key enabler in this process, helping engineering teams optimize performance, power, and reliability simultaneously.

Thei engagement also highlights the value of close collaboration between semiconductor suppliers and the Synopsys Foundation IP team. By combining application expertise with proven IP and advanced characterization capabilities, teams can address requirements that standard solutions may not fully satisfy.

Enabling the Next Generation of Automotive Compute

As vehicles become more intelligent, connected, and software-defined, demand for advanced semiconductor technologies will continue to grow.

Customized Foundation IP helps bridge the gap between advanced process capabilities and automotive-specific requirements. Through proven IP, customization expertise, and close engineering collaboration, automotive semiconductor suppliers can unlock the full value of FinFET technology while maintaining the quality and reliability standards expected in automotive markets.

Summary

FinFET technology offers significant opportunities for automotive semiconductor suppliers pursuing higher performance, greater efficiency, and increased integration. Realizing those benefits, however, requires more than adopting a new transistor architecture.

Customer engagements demonstrate that Synopsys customized Foundation IP, combined with extended characterization, reliability-focused methodologies, and collaborative engineering support from the Synopsys Foundation IP team, can help organizations successfully navigate advanced-node transitions. By optimizing across the design stack, semiconductor companies can accelerate innovation while delivering the reliability and longevity required for next-generation automotive systems.

Visit Synopsys Foundation IP page.

Also Read:

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Rambus Delivers Complete DDR5 Client Chipset for High-Speed CUDIMM and CSODIMM Memory Modules

Rambus Delivers Complete DDR5 Client Chipset for High-Speed CUDIMM and CSODIMM Memory Modules
by Daniel Nenni on 06-09-2026 at 8:00 am

Rambus DDR5 9600 Client Memory Module Chipset

The rapid emergence of AI-enabled personal computers is driving unprecedented demand for higher memory bandwidth, improved signal integrity and greater system reliability. To address these requirements, Rambus has introduced a complete client memory interface chipset for Clocked Unbuffered Dual In-Line Memory Modules (CUDIMMs) and Clocked Small Outline Dual In-Line Memory Modules (CSODIMMs), enabling next-generation AI PCs to fully leverage the performance capabilities of DDR5 memory technology.

As AI workloads become increasingly integrated into mainstream computing platforms, memory subsystems face growing challenges. Large language models, AI assistants, content creation applications and edge inferencing workloads require fast access to large datasets while maintaining low latency and high power efficiency. Traditional memory architectures often encounter signal degradation at higher transfer rates, limiting overall system performance. The new Rambus client chipset addresses these challenges by enhancing signal integrity and enabling higher DDR5 operating speeds.

At the heart of the solution is the Rambus Client Clock Driver (CKD), which serves as a critical timing component for CUDIMM and CSODIMM modules. The CKD re-drives and conditions the clock signal before distribution to the DRAM devices, significantly reducing jitter and timing variations. This architecture improves signal quality across the memory channel, allowing systems to operate reliably at data rates exceeding those achievable with conventional unbuffered memory modules.

The chipset is designed to support the latest JEDEC standards for DDR5 CUDIMM and CSODIMM memory modules. By incorporating clock driver technology directly onto the module, memory manufacturers can overcome electrical limitations associated with increasingly demanding data rates. This capability is particularly important as client systems transition toward DDR5 speeds of 6400 MT/s and beyond, with future generations expected to reach even higher performance levels.

For AI PCs, memory bandwidth plays a crucial role in overall system responsiveness. Modern AI applications frequently move large quantities of data between processors, neural processing units (NPUs), graphics processors and system memory. Bottlenecks in memory throughput can limit the effectiveness of advanced AI accelerators. Rambus’ chipset enables memory modules to sustain higher operating frequencies, helping eliminate these constraints and supporting more efficient AI processing pipelines.

The complete Rambus client chipset includes not only the CKD but also complementary power management and signal conditioning technologies designed to optimize module operation. Together, these components improve timing accuracy, reduce electrical noise and enhance overall memory subsystem reliability. The integrated approach simplifies module design while ensuring compatibility with evolving client platform requirements.

Another important advantage is support for both desktop and mobile form factors. CUDIMMs target desktop and workstation platforms, while CSODIMMs address notebook and compact computing systems. By providing a unified chipset solution across both module types, Rambus enables OEMs and memory manufacturers to standardize development efforts while delivering consistent performance benefits across a broad range of AI PC designs.

Power efficiency remains a key consideration for client computing platforms. AI applications can significantly increase system power consumption, making efficient memory operation essential. The Rambus chipset is engineered to support high-speed operation while minimizing additional power overhead, helping manufacturers balance performance and energy efficiency requirements. This capability is especially valuable in mobile systems where battery life remains a critical design objective.

As the PC industry moves toward widespread deployment of AI-enhanced computing experiences, memory technology will become an increasingly important differentiator. Higher memory speeds, improved reliability and optimized signal integrity are necessary to unlock the full potential of next-generation processors and AI accelerators. Rambus’ complete client chipset provides the foundational technology needed to support these advancements.

Bottom line: By enabling robust DDR5 CUDIMM and CSODIMM implementations, Rambus is helping drive the next wave of AI PC innovation. The company’s expertise in high-speed memory interfaces and signal integrity solutions positions it at the forefront of client memory architecture development, delivering the performance and scalability required for increasingly sophisticated AI workloads.

More information: Scaling to DDR5 9600: Why Clocked Client Memory Modules Matter for AI PCs
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From Evidence to Authority: Bounded Gate Authority for Governed Semiconductor Realization

From Evidence to Authority: Bounded Gate Authority for Governed Semiconductor Realization
by Moh Kolb on 06-09-2026 at 6:00 am

Picture1 BGA JUne2

Advanced semiconductor systems are no longer limited by a single engineering domain. They are constrained by the convergence of many interdependent vectors: silicon nodes, advanced packaging architectures, substrate materials, platform PCBs, power-delivery networks, thermal behavior, manufacturing variation, firmware response, system validation, and long-term lifecycle reliability.

The semiconductor industry has become extremely capable at generating data. EDA tools generate signoff reports. Foundries generate in-line process evidence. OSATs generate assembly and reliability logs. Wafer test creates electrical maps. Package teams measure warpage, coplanarity, interconnect continuity, and thermal resistance. System-validation teams observe workload behavior, voltage droop, timing drift, ECC events, and field failures.

But this data explosion exposes a missing operational layer.

Data alone does not answer the most important question:

Who has the authority to close the decision?

A design may pass simulation. A process may clear in-line metrology. A package may survive initial assembly. A system may boot. A reliability stress test may not yet show failure.

But none of those isolated facts alone proves that the product configuration is mature enough for release.

Modern semiconductor realization requires more than data visibility. It requires bounded decision authority.

That is the role of Bounded Gate Authority inside SEGA-AI™.

Bounded Gate Authority is the controlled decision layer that determines whether normalized, admissible, and causally aligned evidence is sufficient to close, hold, reopen, escalate, or constrain a semiconductor realization decision.

It is the point where evidence becomes authority.

1. Why a New Decision Layer Is Needed

In traditional semiconductor development, decisions were often domain-centered.

The silicon team owned silicon readiness.
The package team owned package readiness.
The board team owned board readiness.
The reliability team owned qualification.
The product team owned customer release acceptance.

That structure worked when physical, electrical, manufacturing, and system boundaries were more separable.

Advanced AI and high-performance computing systems no longer respect those boundaries.

A modern AI accelerator may couple logic die, HBM stacks, silicon interposers, organic or glass-core substrates, EMIB-style bridges, silicon decoupling capacitors, board-level VRMs, firmware-controlled throttling, system telemetry, and field-learning feedback.

Inside this environment, a failure signature may appear in one layer while originating in another.

A leakage failure after package stress may not be a package-only issue. It may involve wafer-level lithography marginality, weak BEOL dielectric, CMP variation, dicing-induced damage, or package stress amplification.

A PDN voltage droop event may not be only a board problem. It may involve silicon switching current, package inductance, decoupling placement, silicon capacitor effectiveness, VRM transient response, and local thermal drift.

A timing drift may appear during system workload, but the root cause may sit across silicon variation, power integrity, thermal gradients, package stress, firmware scheduling, or aging.

The core challenge is not only technical complexity.

It is decision authority under coupled uncertainty.

Teams may have abundant data, but still lack a structured way to decide whether they are authorized to release, hold, rework, retest, reopen, or escalate. This creates decision latency. In severe cases, it creates gate paralysis.

Bounded Gate Authority exists to eliminate that friction.

2. Data Is Not Authority

One of the central principles of SEGA-AI™ is a strict hierarchy:

Data is not evidence.
Evidence is not automatically admissible.
Admissible evidence is not automatically decision authority.

A tool can produce data.
A dashboard can show a green indicator.
A test can pass.
A KPI can appear within limits.

But a realization gate should not close unless the underlying evidence is:

normalized across heterogeneous domains
synchronized across time, lot, tool, configuration, and operating state
traceable to source and ownership
mapped to CTQs
causally aligned through physics-grounded logic
inside the bounded policy envelope
mature enough for the lifecycle phase being authorized

This is why Bounded Gate Authority sits above CEMH and TCG.

CEMH asks:
What maturity level has the evidence reached?

TCG asks:
Can this evidence be trusted as admissible, synchronized, provenance-preserved, and causally valid?

Bounded Gate Authority asks:
Is this evidence sufficient to authorize a consequential decision?

The sequence is simple:

Interoperability moves data.
Admissibility qualifies evidence.
Bounded Gate Authority governs the decision.
Governed convergence closes the system.

3. Defining Bounded Gate Authority

Bounded Gate Authority is the SEGA-AI™ decision layer that determines whether normalized, admissible, and causally aligned evidence is sufficient to close, hold, reopen, escalate, or constrain a semiconductor realization gate.

The word bounded is essential.

SEGA-AI™ is not designed to create unconstrained machine authority. It should not automatically release products, assign final root cause, override expert judgment, or execute corrective action outside approved limits.

Its decision authority must remain inside a defined policy envelope.

That envelope includes:

CTQ requirements
CEMH evidence-maturity thresholds
TCG admissibility requirements
statistical confidence thresholds
stale-data and latency limits
synchronization windows
causality requirements
risk severity levels
allowed corrective actions
human escalation triggers
customer, safety, contractual, or regulatory constraints

Bounded Gate Authority is therefore not blind automation.

It is governed decision-making under uncertainty.

4. Gate Outcomes Cannot Be Binary

A modern semiconductor realization gate cannot operate only as pass or fail.

Complex heterogeneous platforms require more precise outcomes.

Gate outcome Meaning
Close Evidence is mature, admissible, causally aligned, and sufficient to satisfy defined CTQs.
Remain open Evidence is incomplete, immature, stale, conflicting, or not linked to the required CTQs.
Reopen New evidence invalidates a previously closed decision.
Escalate Risk, uncertainty, or cross-domain conflict exceeds the bounded authority envelope and requires human review.
Approve bounded action A limited corrective action is allowed within a pre-validated safe envelope.
Block release A critical CTQ, causality path, or reliability condition remains unresolved.

This is much stronger than a dashboard.

A dashboard reports status.

Bounded Gate Authority determines whether the status is sufficient to support an engineering decision.

5. Decision Latency and Gate Paralysis

Many organizations do not suffer from lack of data. They suffer from unclear authority.

Teams wait for more data.
Then more correlation.
Then another review.
Then more signoff.
Then another cross-functional meeting.

The delay is often interpreted as caution. But in many cases, it is really ungoverned evidence.

In semiconductor realization, indecision often appears as:

  • conflicting wafer and package data
  • green KPIs that do not explain the failure
  • unclear CTQ ownership
  • package failures with possible wafer origins
  • system failures with possible package origins
  • reliability drift after prior gate closure
  • test escapes without causal classification
  • customer pressure without sufficient evidence maturity

This is decision latency.

Bounded Gate Authority converts decision latency into structured action:

  • What evidence is missing?
  • Which CTQ is unresolved?
  • Which causal path is unproven?
  • Which owner must respond?
  • Can the gate remain open?
  • Can a bounded action proceed?
  • Does the issue require escalation?

The goal is not reckless speed.

The goal is governed speed.

6. Root-Cause Governance, Not Root-Cause Replacement

This distinction is critical.

SEGA-AI™ does not replace lithography engineers, failure-analysis labs, package reliability teams, OSAT experts, EDA tools, metrology systems, or process engineers.

It does not magically find the physical root cause.

It governs the root-cause evidence chain.

That matters because the visible location of a failure is not always the point of origin.

A crack may appear after package thermal cycling, but the origin may involve wafer thinning, dicing damage, edge defects, underfill voids, CTE stress, board bending, or a combination of these effects.

A leakage failure may appear during final test, but the origin may involve stochastic lithography defects, weak dielectric formation, BEOL damage, contamination, or package-induced stress.

A timing drift may appear during system workload, but the origin may involve silicon variation, PDN droop, package inductance, thermal gradients, firmware behavior, or localized aging.

A delamination may appear during assembly or reliability testing, but the origin may involve surface preparation, material adhesion, cure profile, moisture exposure, die size, substrate warpage, or thermal cycling.

Bounded Gate Authority prevents premature root-cause assignment.

It forces the organization to ask:

Has the causal path been proven?
Is the evidence admissible?
Are wafer, package, board, firmware, and system logs synchronized?
Did the package create the defect, or expose a latent wafer marginality?
Did the system exceed the validated operating envelope?
Is the evidence strong enough to close the gate, or only strong enough to form a hypothesis?

The correct position is:

SEGA-AI™ does not replace root-cause analysis. It governs when root-cause evidence is mature enough to support a decision.

7. High-NA EUV as a Bounded Gate Authority Example

High-NA EUV is a useful example because it separates technical feasibility from manufacturing authority.

The tool may work. The resolution may be possible. The physics may be demonstrated.

But the production gate cannot close on technical feasibility alone.

A bounded gate must ask whether the entire manufacturing ecosystem has generated enough mature evidence to authorize high-volume insertion.

That includes evidence for:

  • depth-of-focus margin
  • wafer topography sensitivity
  • stochastic defect control
  • resist maturity
  • mask architecture
  • pellicle thermal reliability
  • metrology and inspection capability
  • tool uptime
  • etch and deposition interaction
  • defect-to-yield translation
  • customer economics

A conventional decision might ask:

Can High-NA print smaller features?

A SEGA-AI™ bounded gate asks:

Is the High-NA evidence mature, admissible, and economically sufficient to authorize production insertion?

That is the difference between technical capability and realization authority.

8. CoWoP and the Transition-Patch Gate

CoWoP provides a second example.

At first glance, CoWoP appears to shorten the system path:

Die / HBM → interposer / wafer-level structure → platform PCB

But that is too simple.

The critical challenge is the pitch transition from silicon/interposer scale to PCB scale. A silicon interposer or advanced hybrid-bonding interface may operate at roughly 10–40 µm pitch, while a platform PCB attach environment may operate closer to hundreds of microns.

That gap cannot be treated as a simple direct landing problem.

A more realistic CoWoP path may require an intermediate transition structure:

Die / HBM → wafer-level interposer → transition redistribution patch → platform PCB

The transition patch becomes the governed bridge between wafer-level precision and platform-level manufacturability.

It may be implemented as a thin glass-core transition patch, using TGVs and stable CTE behavior to support fine-pitch registration and vertical fan-out. Or it may be implemented as a high-density organic patch, using compliance to absorb stress between the rigid interposer structure and the larger PCB.

The gate question is not:

Can the interposer connect to the PCB?

The real gate question is:

Can the interposer-to-transition-patch-to-platform-PCB corridor remain electrically, mechanically, thermally, manufacturably, and operationally converged?

That gate cannot close until evidence exists for:

  • pitch translation
  • pad registration
  • TGV or via reliability
  • glass/copper or organic/copper stress
  • CTE continuity
  • attach fatigue
  • PDN impedance
  • return-path continuity
  • UCIe crosstalk
  • DDR/LPDDR timing
  • VRM transient response
  • thermal cycling
  • inspection and rework
  • lifecycle reliability

This is exactly why Bounded Gate Authority is needed.

The architecture may be promising, but the gate remains open until the corridor evidence becomes admissible.

9. Glass Substrates and the Danger of Overclaiming

Glass-core substrates are another important example.

Glass can improve dimensional stability, reduce warpage, support better CTE control, and strengthen vertical power-delivery paths through TGVs.

That is meaningful.

But glass does not eliminate the package problem.

In most glass-core substrate architectures, the glass is mainly the core. Build-up layers still remain. High-speed routing is still concentrated near the top build-up structure. Bottom-side routing through TGVs is not equivalent to short top-side interconnect. TGVs still introduce discontinuities, stress, reliability concerns, and inspection requirements.

Therefore, the bounded gate should not ask only:

Does glass improve the substrate?

It should ask:

Does this glass-core substrate have enough evidence to close the realization gate for this specific package, pitch, power, thermal, manufacturing, and reliability envelope?

Without Bounded Gate Authority, teams may overgeneralize material benefits.

With Bounded Gate Authority, every material improvement must still prove its CTQ contribution inside the full realization corridor.

10. Silicon Capacitors, EMIB, and the PDN Gate

Silicon capacitors near bridges, interposers, or package structures are becoming important because AI accelerators are increasingly limited by power integrity.

Closer decoupling can reduce impedance, improve transient response, and help manage dI/dt events.

But closer capacitance does not automatically close the power-delivery gate.

The bounded gate question is:

Does the full PDN corridor remain inside the allowed envelope across workload, temperature, package variation, and lifecycle drift?

That requires evidence for:

  • impedance profile
  • voltage droop response
  • anti-resonance
  • current return path
  • capacitor aging
  • thermal stress
  • bridge reliability
  • package warpage interaction
  • HBM/logic transient coupling
  • field drift

A silicon capacitor may improve the PDN.

But the gate closes only when the full PDN evidence is mature, admissible, and causally aligned.

11. Certified EDA Flows Generate Evidence, Not Authority

Foundry-certified EDA flows are essential.

Physical verification, EM/IR analysis, reliability checking, photonic IC verification, analog/mixed-signal simulation, 3D IC planning, and advanced-package integration all generate important evidence.

But certification does not automatically close the realization gate.

A qualified EDA/foundry flow can generate trusted evidence. SEGA-AI™ asks whether that evidence is:

  • normalized across domains
  • mapped to the correct CTQs
  • synchronized with manufacturing and package state
  • causally aligned with expected system behavior
  • mature enough to support release authority

This distinction is important.

SEGA-AI™ does not replace EDA signoff. It extends the decision boundary from design signoff to governed realization.

12. Firmware–Hardware Handshake and Bounded Action

Bounded Gate Authority also applies after design-time and manufacturing release.

In the Firmware–Hardware Handshake model, hardware senses runtime state, firmware executes bounded actions, and governed evidence determines whether the action is valid.

For example:

Can voltage be adjusted?
Can a lane be retrained?
Can a tile be throttled?
Can a link enter degraded mode?
Can a workload migrate?
Can a gate reopen based on field drift?

These actions should not be authorized just because a sensor changed.

They should be authorized because the evidence is admissible, synchronized, causally meaningful, and inside the approved response envelope.

This is Bounded Gate Authority in runtime form.

13. How Bounded Gate Authority Fits the SEGA-AI™ Stack

The SEGA-AI™ foundation sequence becomes clearer:

GFL — Governance for Lifecycle
Defines why semiconductor systems need governance beyond design-time and release.

TCG — Trusted Convergence Governance
Defines why evidence must be trusted, synchronized, provenance-preserved, and admissible.

CEMH — Convergence Evidence Maturity Hierarchy
Defines how raw data becomes interoperable data, normalized evidence, admissible evidence, and convergence-authoritative evidence.

Bounded Gate Authority
Defines how mature evidence becomes an authorized decision.

The relationship is:

  • GFL defines the lifecycle mission.
  • TCG protects evidence integrity.
  • CEMH measures evidence maturity.
  • Bounded Gate Authority decides what can be closed, held, reopened,
  • escalated, or acted upon.

This is the missing governance layer.

Without Bounded Gate Authority, SEGA-AI™ risks looking like an evidence dashboard.

With Bounded Gate Authority, SEGA-AI™ becomes a governed decision architecture.

14. The Bounded Gate Authority Decision Flow

A practical flow can be defined in ten steps.

  1. Define CTQs
    Identify the critical-to-quality attributes for the gate: electrical, thermal, mechanical, manufacturing, reliability, firmware, or lifecycle.
  2. Collect evidence objects
    Gather evidence from EDA, simulation, fab, metrology, wafer test, OSAT, package reliability, system validation, firmware logs, and field telemetry.
  3. Normalize evidence
    Convert evidence into a common schema with units, context, time, lot, die location, tool, material, process, ownership, and configuration metadata.
  4. Evaluate maturity through CEMH
    Determine whether the evidence is raw data, interoperable data, normalized evidence, admissible evidence, or convergence-authoritative evidence.
  5. Apply TCG filters
    Check provenance, synchronization, realization-state consistency, causality, and trustworthiness.
  6. Map evidence to CTQs
    Confirm that the evidence actually supports the gate decision, not just adjacent metrics.
  7. Evaluate uncertainty
    Identify stale data, conflicting data, missing domains, insufficient sample size, or unresolved failure signatures.
  8. Apply bounded authority rules
    Determine whether the decision is inside the allowed policy envelope.
  9. Issue gate outcome
    Close, remain open, reopen, escalate, approve bounded action, or block release.
  10. Preserve the decision trace
    Record evidence objects, CTQs, assumptions, owners, uncertainty, reasoning, and follow-up requirements.

This is how SEGA-AI™ turns evidence into decision authority.

15. Why This Matters for Semiconductor Leadership

The semiconductor industry is entering an era where leadership will not be determined only by better devices, smaller nodes, or more expensive tools.

It will be determined by better realization decisions.

Advanced packaging is becoming the system-level scaling engine. AI accelerators are pushing package size, HBM integration, PDN complexity, thermal density, substrate capability, and board-level interaction. CoWoP may move the platform PCB into the active realization corridor. High-NA EUV may challenge the boundary between lithography capability and production maturity. Quantum computing may move from laboratory physics into wafer-scale manufacturing and system deployment.

In each case, the question is not only:

Can the technology work?

The question is:

Can the technology be released, scaled, monitored, and trusted?

That is a gate-authority question.

Bounded Gate Authority gives SEGA-AI™ the language to answer it.

Conclusion

Modern semiconductor realization does not fail only because teams lack data.

It fails when data cannot be converted into trusted evidence, when evidence cannot be mapped to causality, when causality cannot be tied to CTQs, and when no bounded authority exists to close, hold, reopen, escalate, or constrain the decision.

That is why Bounded Gate Authority is a necessary foundation layer for SEGA-AI™.

It does not replace engineering judgment.
It does not replace failure analysis.
It does not replace EDA, metrology, lithography, OSAT, package reliability, or system validation.

It governs the decision boundary between them.

The future of semiconductor realization will depend on more than tools, dashboards, and KPIs. It will depend on whether organizations can determine when evidence is mature enough to act.

Interoperability moves data.
Admissibility qualifies evidence.
Bounded Gate Authority governs the decision.
Governed convergence closes the system.

Also Read:

Convergence Evidence Maturity Hierarchy: From Raw Data to Convergence-Authoritative Evidence

Trusted Convergence Governance: Preserving Admissibility Integrity Across Heterogeneous Semiconductor Systems

Closing the Silicon Realization Gap: From Static DFM to Governance for Lifecycle (GFL)

Beyond Tool Interoperability: The Emerging Governed Convergence Problem in Semiconductor Design

Closing the Reality Gap: A New Architecture for 1.8-Tb/s Chiplet Governance


Synopsys and Samsung Foundry Extend AI-Driven Design Collaboration for Advanced 2nm and Multi-Die Systems

Synopsys and Samsung Foundry Extend AI-Driven Design Collaboration for Advanced 2nm and Multi-Die Systems
by Daniel Nenni on 06-08-2026 at 10:00 am

synopsys samsung safe 2026 news announcement 1600x900

At SAFE Forum 2026, Synopsys announced significant advancements in its collaboration with Samsung Foundry, expanding AI-powered design, verification, test, and IP solutions for Samsung’s most advanced process technologies. The announcement underscores the growing importance of electronic design automation (EDA), design technology co-optimization (DTCO), and multi-die methodologies as semiconductor companies push toward increasingly complex AI, HPC, automotive, and data-center applications.

A key focus of the collaboration is support for Samsung Foundry’s second- and third-generation 2nm process technologies. Synopsys has delivered production-ready digital and analog implementation flows optimized for these nodes, enabling customers to achieve improved power, performance, and area (PPA) while accelerating time-to-market. The flows leverage Synopsys’ AI-driven optimization technologies to automate design exploration and implementation tasks that have traditionally required extensive engineering effort.

The companies highlighted several DTCO initiatives that integrate process technology knowledge directly into synthesis, physical implementation, and signoff flows. By tightly coupling Samsung’s advanced process characteristics with Synopsys implementation tools, customers can optimize designs earlier in the development cycle and achieve measurable PPA improvements. These DTCO methodologies are increasingly critical as advanced-node scaling introduces new challenges related to power delivery, variability, thermal effects, and manufacturing complexity.

Multi-die design was another major theme of the announcement. As AI accelerators and high-performance computing devices exceed the practical limits of monolithic SoCs, semiconductor developers are increasingly adopting chiplet-based architectures. Synopsys and Samsung Foundry have expanded their collaboration to support advanced packaging and 3D integration technologies through integrated design flows that span silicon, package, and system domains. The companies are enabling scalable multi-die implementations using certified multiphysics signoff solutions within Synopsys 3DIC Compiler, allowing designers to evaluate electrical, thermal, and mechanical interactions across heterogeneous die assemblies.

The collaboration also reflects a broader shift toward system technology co-optimization (STCO), which extends beyond traditional DTCO by considering interactions among dies, interconnects, packaging technologies, power delivery networks, and thermal management. This holistic approach is particularly important for AI workloads that demand unprecedented compute density, memory bandwidth, and energy efficiency. By enabling system-level analysis earlier in the design process, Synopsys and Samsung Foundry aim to help customers reduce design iterations while improving overall system performance.

Beyond design implementation, Synopsys showcased advancements in AI-powered test and manufacturing solutions. The companies reported that customers are achieving test-efficiency improvements of up to 20% through the use of silicon-based, AI-driven test optimization technologies validated on Samsung Foundry processes. These capabilities reduce test patterns and test-cycle requirements while maintaining fault coverage, contributing to lower manufacturing costs and improved production throughput for advanced SoCs and multi-die devices.

The ecosystem expansion also includes a broader portfolio of certified interface IP and foundation IP optimized for Samsung’s advanced process nodes, including automotive technologies. As AI and automotive applications increasingly require high-bandwidth connectivity, functional safety, and robust security features, access to production-proven IP becomes a critical enabler for faster design deployment and reduced integration risk.

Bottom line: Presented under the SAFE Forum 2026 theme, “The Nexus for Silicon Intelligence,” the collaboration demonstrates how AI-driven EDA, advanced process technologies, and heterogeneous integration are converging to address the escalating complexity of next-generation semiconductor systems. Through deeper co-optimization across design, packaging, test, and manufacturing, Synopsys and Samsung Foundry are providing customers with a comprehensive platform for developing the AI and multi-die architectures that will power future computing infrastructure.

Synopsys and Samsung Foundry Extend AI-Driven Design Collaboration for Advanced 2nm and Multi-Die Systems

Also Read:

The Great Divide: A Tale of Three Hardware Emulation Architectures

Synopsys and TSMC Deepen AI Design Alliance: What It Means

How to Overcome the Advanced Node Physical Verification Bottleneck


Broadcom Told the Truth. The Market Hasn’t Heard the Rest of It Yet.

Broadcom Told the Truth. The Market Hasn’t Heard the Rest of It Yet.
by Jonah McLeod on 06-08-2026 at 8:00 am

Black Friday

Hock Tan and his CFO Kirsten Spears logged into the June 3 earnings call with numbers that should have satisfied anyone. AI semiconductor revenue hit $10.8 billion in Q2, up 143% year over year, above Broadcom’s own forecast. Full-year AI guidance went to $56 billion. The $100 billion fiscal 2027 target was reaffirmed. By any prior measure, a blowout quarter. The stock dropped 12% in after-hours trading anyway, and by Friday had taken the Nasdaq down with it.

The reason wasn’t the numbers. It was the Q&A.

The first crack in the dyke appeared when Blayne Curtis from Jefferies asked the question every analyst in the room wanted answered but none wanted to ask directly. He’d noticed the 8-K disclosing a long-term agreement with Google and got to the point: “I think there is a lot of concern about share within that customer. I was just kind of curious, now that you have this agreement, maybe you could speak to a little bit more in terms of your confidence.”

[Broadcom Q2 2026 Earnings Call Transcript]

Tan called it “a very, very strong agreement” reflecting “the strength of the partnership we have.” Then he said the quiet part out loud: “We also accept the fact that given the rate of growth of consumption and development of AI compute by our partner Google, that we fully expect that there will be some diversity of sources for them.” The market heard the hedge, not the hype. Broadcom’s biggest customer was spreading its bets across multiple chip suppliers to maintain negotiating leverage, control its own technology roadmap, and avoid the sole-source dependency that hands a supplier pricing power over time. Every analyst on the call started mentally discounting the $100 billion target.

The broadening breach in the dyke came from Ross Seymore at Deutsche Bank, who pressed on margins. Gross margin was falling, and he wanted to know why within semiconductors specifically. CFO Kirsten Spears explained the mix dynamics; ASICs and TPUs carry lower margins, AI networking offsets some pressure. Then Tan stepped in: “Structurally, the semiconductor margins remain very stable and very solid. It is the mix, particularly the mix between software and non-AI to the very, very rapidly growing AI semiconductor, that is just diluting gross margin.”

Translation: the faster Broadcom grows its AI chip business, custom ASICs designed exclusively for individual hyperscaler customers: Google’s TPUs, Anthropic and OpenAI accelerators, Meta’s MTIA chips, the worse its blended margins get. That’s not a temporary problem. Spears had already told investors to model the two segments separately going forward, a CFO’s way of saying the consolidated numbers are going to look worse as AI scales.

Put those two disclosures together and layer on top the Q3 AI guidance of $16 billion, below analyst expectations of $17.2 billion, and you have a selloff. Any one of those three was manageable. Together they told a story: growth may be decelerating at the margin, the biggest customer is hedging, and the business growing fastest is the least profitable. Investors who had priced Broadcom as the pure-play AI infrastructure winner had to reconsider all three assumptions simultaneously, in real time, on a Wednesday afternoon.

What makes the margin confession significant, is Jensen Huang had said precisely the same thing two days earlier at Computex, but said it differently. He described AI factories as the largest infrastructure buildout in human history, with single sites heading toward one gigawatt and capital costs of “$50 billion to $60 billion, and soon it will be $80 billion to $100 billion per gigawatt.” He was explicit about where value accumulates in that world: “If you have one gigawatt of power, then throughput per watt is revenues, because every token is profitable, every token is revenues.”

The unit of competition is no longer the chip. It is the system, watts, racks, networking, cooling, optimized to generate the maximum tokens per dollar of capital deployed. Huang was direct about what Nvidia had become: “A long time ago, Nvidia used to be a GPU company, but over the years we’ve evolved to become a systems company. Nvidia has really started to transform ourselves yet again” into an AI infrastructure company that helps customers build entire AI factories, not just buy servers.

Marvell reported four days before Broadcom and gave the market every reason to separate the two. Its optical interconnect and networking business, the pipes between the engines, held margins firm at 58.9% non-GAAP while revenue grew 28% year over year. CEO Matt Murphy upgraded interconnect revenue growth guidance three times in a single call: “It was beginning of this year 30%, then 50% and now 70%.” He was direct about why: “Our networking products, including interconnect and switching, are driving strong revenue growth as networking becomes increasingly critical with each new generation of AI infrastructure. Now in the early stages of generative AI, the primary focus was on addressing compute and memory bottlenecks. As more complex architectures such as reasoning models and mixtures of experts have begun to deploy, the role of networking has become significantly more important.”

[Marvell Q1 2027 Earnings Call Transcript]

The market didn’t care. When Broadcom fell, Marvell fell with it, down 16.74% on the day, the second largest loser in the semiconductor complex. ARM fell 12.84%. Micron dropped 13.25%. A stock up 145% year to date, with margins holding and guidance rising, got sold in the same undifferentiated sweep that took down every name with “semiconductor” and “AI” in its description. Charu Chanana, chief investment strategist at Saxo, told Reuters the selloff reflected a market that had run too far on a single thesis: South Korea had been “one of the biggest beneficiaries of the AI memory supercycle,” making the entire region vulnerable when investors began to question whether expectations around AI demand had run too far ahead of reality.

The same logic applied in Santa Clara. That indiscriminate selling is the most dangerous signal in the entire episode, not because it was wrong about Broadcom, but because it proves the market is reading the sector as a category rather than a supply chain. It cannot yet separate the engines from the pipes, the compute layer from the interconnect layer, companies whose margins are compressing from those whose margins are not. When wave two, that’s the design services exposure in India and Israel, surfaces in an earnings call the same category logic will apply. Everything will get sold. The distinctions that matter will be the last thing priced.

Tan’s margin compression and Murphy’s margin expansion are the same story told from opposite ends. Broadcom’s compute silicon is diluting blended margins as it scales. Marvell’s interconnect layer is holding margin and accelerating. The chips that move data are worth more than those that process it. It’s where the AI buildout’s economics comes to rest, confirmed simultaneously by two CEOs on consecutive earnings calls. The semiconductor industry has spent two years pricing the AI boom as a chip story. Computex said it was an infrastructure story. The Broadcom and Marvell earnings calls confirmed it with gross margin data in the same week. The selloff  was the market updating a model that had assumed the chip was where the money was. Two CEOs in Palo Alto, one in Santa Clara, and Jensen Huang on a stage in Taipei all said it wasn’t. Friday was the market catching up.

Nvidia’s position in this story is more complicated. Nvidia does not compete with Broadcom for hyperscaler custom silicon programs. It sells merchant silicon, H100, Blackwell, Rubin, to anyone who will buy it, amortizing R&D across the entire addressable market and running gross margins above 70% because the same chip ships to thousands of customers simultaneously. The hyperscaler custom silicon buildout at Broadcom exists to reduce Nvidia dependency. Google has been running its own TPUs since 2016 for that reason. Huang’s Computex shift from GPU company to AI factory company is his answer to that displacement risk.

But the direction change reveals something more important about where Nvidia’s actual growth market exists. The AI factory pitch wasn’t aimed at Google or Microsoft. They have their own compute ecosystem. Huang was pitching to the wildcatters: sovereign wealth funds building national AI compute, regional cloud providers, petrostates erecting data centers in the Gulf, CoreWeave, xAI, enterprise buyers who want hyperscaler-grade AI capability without hyperscaler dependency or the engineering organization to build from scratch.

These customers cannot design their own TPUs. They need the whole system pre-integrated and they will pay Nvidia’s margin to get it. The problem is that wildcatter data centers are the least equipped customers to absorb an India shock. A sovereign AI facility in Saudi Arabia or Gujarat doesn’t have Google’s engineering organization to run it. It depends entirely on the Indian execution layer: the cloud operations, DevOps, systems integration, and data engineering talent, that Jane Hsu, founder of Researcher and Research LLC identified as the unmodeled dependency. Nvidia’s fastest-growing customer segment is the most exposed node in the chain nobody is watching.

Whether the contagion spreads beyond Broadcom and Marvell depends on a question nobody on the June 3 earnings call thought to ask. The selloff was wave one, the market repricing the assumption that custom silicon margins would hold as AI scales. Wave two hasn’t hit yet. It exists in the design services layer beneath the chip companies: the Indian and Israeli engineering firms doing the RTL design, functional verification, and physical implementation work that turns a hyperscaler’s AI ambition into manufacturable silicon. Their exposure doesn’t show up in a Bloomberg terminal until a program slips or a hiring freeze surfaces in an earnings call.

Wave three is the hyperscalers themselves. If custom silicon timelines extend because design services capacity is constrained or disrupted, hyperscaler AI infrastructure schedules slip, CapEx efficiency falls, and AI revenue assumptions built into valuations that survived Friday’s selloff come back into question. The containment scenario requires hyperscaler internalization, Amazon’s Trainium, Microsoft’s Maia, Meta’s MTIA, to move faster than the macro risks materialize. The problem is that those internalization programs are using the same Indian and Israeli design services firms. The diversification strategy and the concentration risk share the same human capital base. The cure and the disease have the same address.

Intel makes the geographic concentration argument concrete from both sides simultaneously. Its foundry business is competing, so far without significant hyperscaler traction, to manufacture the custom silicon that Broadcom and Marvell currently dominate. But Intel’s most productive chip design operation outside the United States is not in India. It is in Haifa and Petah Tikva, where the team that designed the Core microarchitecture has worked for decades. Intel’s Israeli engineering centers remain among the company’s most strategically important assets, the kind of concentration that would appear nowhere in a standard supply chain risk assessment because it has never had to.

Israel has been stable enough, for long enough, that nobody modeled what happens if it isn’t. That assumption is now being tested in real time. Kristian Kerr, head of macro strategy at LPL Financial, noted that investors may be underestimating how difficult it could be to restore shipping through the Strait of Hormuz to pre-war levels even if Washington and Tehran reach an agreement.  Any initial improvement would come from clearing existing bottlenecks, not a sustained restart in production. The same logic applies to engineering capacity. You don’t restart a semiconductor design program the way you clear a stranded cargo. Two nodes in the custom silicon design chain, India’s implementation layer and Israel’s architecture layer, are simultaneously exposed to the same geopolitical event, and neither exposure appears in a hyperscaler risk filing. Intel knows where its engineers are. Its customers apparently do not think to ask.

What none of the analysts on the Broadcom call mentioned, and the roster was JPMorgan, Jefferies, Deutsche Bank, UBS, Goldman Sachs, Morgan Stanley, Barclays, Bernstein, Melius, Cantor Fitzgerald, Citi, and Charter Equity Research, firms collectively managing trillions in semiconductor exposure, is the layer beneath the infrastructure. Not one asked about the human capital the AI buildout depends on. Jane Hsu, founder of Researcher and Research LLC, draws a line the models miss: the buildout is a physical capacity story, GPUs, data centers, power, networking. Infrastructure, on the other hand is not the same as utilization. It runs on engineering talent, cloud operations, DevOps, data engineering, and systems integration. India is neither an AI consumer market nor a cost-reduction play. It is a structural node in the execution layer the buildout depends on. The risk is not that the data centers fail to be built. It is that implementation, adoption, and enterprise utilization arrive more slowly than the physical buildout assumes.

The chain runs deeper than the earnings calls suggest. The hyperscaler custom ASIC business, the Google TPUs, the Anthropic and OpenAI accelerators, the Meta MTIA chips that Tan enumerated on the call, does not run on American engineers alone. The detailed implementation work between chip architecture and physical silicon flows substantially through Indian design service firms. Tata Consultancy Services, Wipro, HCL, and specialist semiconductor design houses like Sasken and L&T Technology Services do the RTL design, functional verification, physical implementation, and timing closure work that turns a hyperscaler’s AI ambition into manufacturable silicon.

This is advanced semiconductor engineering on leading-edge nodes, and India has built a thirty-year deep capability in precisely these disciplines. When Tata Elxsi and KPIT fell on the NSE in sympathy with Broadcom on Friday, the market was not reacting to sentiment. It was beginning to price the dependency. An India shock doesn’t just slow enterprise IT implementation, it potentially slows the custom silicon programs Tan and Murphy identified as their primary growth engines. The ribbon cuttings are American. The engineering is not.

The data centers get built. The demand they were built to serve arrives late or not at scale. That gap between infrastructure investment and utilization reality is where valuations go to die. Broadcom’s earnings call put a number on it. Hock Tan called it gross margin compression. The market responded with a trillion-dollar selloff. The human capital layer, the engineers in Bengaluru, Hyderabad, Pune, Haifa, and Petah Tikva who turn a data center full of GPUs into a functioning enterprise system, is the part of the story none of them addressed, because none of them has modeled it as a risk.

Nobody rang a bell in July 1997 either.

Also Read:

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Who’s Buying America’s Foundry Future?


Weebit Nano ReRAM Reaches Commercial Tape-Out Milestone

Weebit Nano ReRAM Reaches Commercial Tape-Out Milestone
by Daniel Nenni on 06-08-2026 at 6:00 am

Weebit Nano ReRAM Reaches Commercial Tape Out Milestone

Weebit Nano has achieved a critical milestone in the commercialization of Resistive Random Access Memory (ReRAM) technology with the successful tape-out of two customer products integrating its embedded non-volatile memory IP. One of the products has already returned first silicon and demonstrated functional operation, validating both the manufacturability and operational integrity of the company’s ReRAM technology in real semiconductor devices.

The announcement represents more than a standard product update. In semiconductor development, tape-out is the final stage in the integrated circuit design process before fabrication begins. Once a design is taped out, the layout data is transferred to the foundry for mask generation and wafer manufacturing. Achieving this stage with multiple customers demonstrates that Weebit Nano’s ReRAM has progressed beyond laboratory validation into production-oriented semiconductor integration.

The first disclosed customer, Overlord Labs, integrated Weebit Nano’s ReRAM into a next-generation smart battery management system fabricated at DB HiTek. Smart battery management systems require highly reliable embedded memory capable of operating under demanding thermal and electrical conditions while consuming minimal power. Traditional embedded flash solutions become increasingly difficult to scale at advanced process nodes because of high programming voltages, process complexity, and integration costs. ReRAM offers a compelling alternative due to its low-power operation, simplified process integration, and high endurance characteristics.

Weebit Nano’s ReRAM architecture is based on resistive switching mechanisms that alter the resistance state of memory cells using conductive filament formation within dielectric materials. Unlike floating-gate flash memory, ReRAM does not require charge storage in insulated gates. This enables lower write voltages, faster switching speeds, and improved scalability for advanced nodes. The technology also supports back-end-of-line compatibility, reducing disruption to existing CMOS manufacturing flows.

According to the company, the Overlord Labs device is expected to deliver improvements in power consumption, reliability, and overall system efficiency once manufacturing and qualification are completed. These characteristics are particularly important in battery-powered and edge-computing applications where energy efficiency and data retention directly affect operational performance and product lifetime.

The second customer product has already reached a more advanced stage, with first silicon prototypes fabricated and tested. Initial electrical characterization confirmed that both the system functionality and embedded ReRAM blocks are operating as expected. This is a significant achievement because embedded memory integration often introduces process interactions that only become visible after fabrication. Functional first silicon substantially reduces technical risk and accelerates the path toward product qualification.

Although the successful prototype demonstration validates the technology, commercial semiconductor products still require extensive qualification before mass production. The company indicated that customers will continue characterization, reliability testing, and qualification processes that may take between 12 and 18 months. These evaluations typically include endurance cycling, data retention analysis, high-temperature operating life testing, electromigration studies, and radiation susceptibility assessments depending on target markets.

From a manufacturing perspective, Weebit Nano’s progress also demonstrates increasing foundry readiness for ReRAM adoption. Semiconductor foundries and integrated device manufacturers are actively searching for alternatives to embedded flash as process scaling becomes more challenging below 28nm geometries. Embedded flash integration at smaller nodes often requires additional masks and specialized process steps that increase wafer costs and complexity. ReRAM’s simpler integration profile provides economic advantages while supporting improved memory density and lower operating power.

The technology is particularly attractive for applications such as artificial intelligence inference accelerators, automotive microcontrollers, industrial automation systems, secure edge devices, and analog or power integrated circuits. AI edge systems benefit from non-volatile memory capable of retaining neural network parameters while minimizing standby power consumption. Automotive systems require high reliability across wide temperature ranges, while industrial systems prioritize endurance and long operational lifetimes.

Weebit Nano also highlighted that several additional customers are currently integrating its ReRAM IP into next-generation designs, with further tape-outs expected during the calendar year. The expansion of customer engagements suggests growing industry confidence in ReRAM as a commercially viable embedded memory technology.

Historically, emerging memory technologies have struggled to transition from research demonstrations to manufacturable semiconductor products because of integration complexity, reliability concerns, or insufficient ecosystem support. The successful tape-out and first silicon validation reported by Weebit Nano indicate that ReRAM may now be entering a meaningful commercialization phase. If qualification milestones continue successfully, the technology could become an increasingly important replacement for embedded flash memory in future low-power and high-performance semiconductor platforms.

CONTACT Weebit Nano

Also Read:

2026 Outlook with Coby Hanoch of Weebit Nano

Weebit Nano Reports on 2025 Targets

Weebit Nano Moves into the Mainstream with Customer Adoption


CEO Interview with Chuck Gershman of Owl Autonomous Imaging

CEO Interview with Chuck Gershman of Owl Autonomous Imaging
by Daniel Nenni on 06-07-2026 at 4:00 pm

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Chuck Gershman is the CEO and co-founder of Owl Autonomous Imaging. He has spent more than 30 years in semiconductors, AI imaging, computer vision, and autonomous sensing technologies across executive management, engineering, marketing, business development, and operations.

Chuck is a Drexel University College of Engineering Alumni Circle of Distinction inductee, which is the highest honor bestowed upon alumni by the College. Earlier in his career he was honored as a finalist for EE Times’ ACE Award for High Technology Executive of the Year and was recognized by Medical Marketing & Media as a Top 40 Healthcare Transformer for contributions to Clinical AI Decision Support technologies.

Over the course of his career he has helped lead multiple technology companies through growth and acquisition, including exits to Intel and PMC-Sierra. Chuck also hold multiple U.S. patents related to semiconductor architecture and AI-enabled imaging systems.

The Owl mission has always been straightforward: fundamentally improve machine perception under the real-world conditions where existing sensors fail.

What is the backstory behind Owl Autonomous Imaging?

The foundation of Owl’s technology originated from a U.S. Air Force challenge grant involving precision tracking of ballistic missile systems. That effort led to the development of core thermal ranging and perception technologies which eventually evolved into Owl’s broader thermal computer vision platform.

From the beginning, we recognized that thermal imaging had enormous untapped potential beyond traditional night vision applications. Thermal sensors inherently see living objects exceptionally well and maintain performance independent of ambient lighting conditions. However, legacy thermal systems were historically constrained by low resolution, analog architectures, high cost, and limited scalability.

Owl was founded in 2019 to solve those limitations.

Our team combines deep semiconductor expertise, thermal imaging expertise, AI perception expertise, and advanced camera system experience. Several members of the team previously contributed to the development of the world’s first commercial digital cameras and optical scanning systems. Members of the team have also developed thermal imaging systems deployed in space as well as advanced military-grade thermal imaging systems fielded to date.

Today Owl is focused on building a modern, digitally architected thermal perception platform designed for scalable autonomy applications across both defense and commercial markets.

What core problems is Owl solving?

Modern autonomous systems and emerging Physical AI platforms still struggle with one of the most fundamental challenges in perception: reliable detection, classification, and spatial understanding under degraded visual conditions.

Conventional cameras work well during ideal daylight conditions but fail rapidly in darkness, glare, smoke, fog, rain, snow, dust, or battlefield obscurants. Radar provides range but lacks spatial fidelity. LiDAR delivers useful depth information but remains challenged by cost, SWaP constraints, weather susceptibility, and scalability into truly mass-deployed autonomous systems.

As Physical AI moves beyond controlled environments into real-world operation, the sensing challenge becomes significantly more difficult. Autonomous systems must not only perceive objects, but understand and react to dynamic environments reliably, day and night, and under adverse environmental conditions.

Owl addresses this problem through high-resolution thermal perception combined with AI-driven ranging, classification, and scene understanding.

Our systems are designed to provide dense thermal perception and precision ranging regardless of lighting condition or environmental degradation. This capability is increasingly important across autonomous drones, robotic mobility, defense systems, industrial autonomy, and future automotive safety platforms.

As autonomous systems proliferate, the requirement is no longer simply achieving autonomy in ideal conditions. The requirement is achieving autonomy reliably at scale, in real-world operating environments, at economically deployable cost structures.

That is where Owl is focused.

Owl originally became known for automotive thermal perception. How has the company evolved?

Automotive safety and pedestrian automatic emergency braking remain important long-term markets for Owl, and we continue to believe thermal perception will ultimately become a critical sensor modality for next-generation safety systems.

However, the broader autonomy market has evolved significantly over the last several years.

Today we are seeing major demand pull from defense and dual-use autonomous systems, particularly unmanned aerial systems, robotic mobility, perimeter security, and autonomous sensing platforms operating in difficult environmental conditions.

This market transition aligns extremely well with Owl’s core strengths.

The defense community increasingly recognizes that future autonomous systems must be:

  • Low SWaP-C (Size, Weight, Power, and Cost)
  • Highly manufacturable
  • Scalable into volume production
  • Power efficient
  • Operational day and night
  • Effective in degraded visual environments
  • Cost optimized for attritable deployment models

These requirements map directly into Owl’s semiconductor-first architecture approach and are enabled through Owl’s integrated hardware and software platform strategy.

Our KnightOwl™ product family delivers advanced thermal camera core and focal plane array hardware optimized for low SWaP autonomous platforms, while KnightVision™ provides Owl’s AI-enabled perception and software stack for thermal ranging, object classification, tracking, localization, and autonomous scene understanding.

We believe the industry is transitioning from legacy thermal architectures optimized for premium low-volume military systems toward digitally architected thermal platforms capable of scaling more like modern semiconductor products.

That shift creates a significant opportunity for Owl.

What makes Owl’s technology and products unique?

Owl has developed a fundamentally different thermal imaging architecture built around a digital-first focal plane design philosophy.

Historically, thermal imaging systems have relied heavily on analog readout architectures that impose limitations on power, scalability, calibration complexity, manufacturability, and imaging performance.

Owl’s architecture moves substantial functionality into the digital domain directly at the focal plane level. This approach enables significantly lower system power, improved scalability, reduced calibration complexity, higher frame rates, and substantially improved manufacturability relative to traditional thermal architectures.

Our KnightOwl™ thermal imaging product family leverages megapixel-class digital focal plane architectures capable of delivering HD thermal imaging with extremely low SWaP profiles suitable for autonomous platforms, drones, robotic systems, and next-generation defense applications.

In parallel, Owl’s KnightVision™ software platform provides advanced AI-enabled thermal perception, ranging, classification, localization, tracking, and scene understanding capabilities for next-generation Physical AI and autonomous system stacks.

Importantly, our systems are designed from the outset around scalability and production economics rather than exclusively optimizing for niche premium military deployment models.

We believe this combination of semiconductor-first thermal architecture, AI-enabled perception, and manufacturable system design positions Owl uniquely for the next generation of scalable autonomous systems.

Why is manufacturability and scale becoming so important in thermal imaging?

The global defense environment has changed dramatically.

The emergence of autonomous drones, attritable systems, autonomous swarming, and distributed sensing architectures has fundamentally altered procurement assumptions across defense markets.

Historically, thermal imaging systems were optimized for exquisite, low-volume platforms where cost and manufacturing scalability were secondary considerations.

That model does not scale effectively into emerging autonomy and drone deployment paradigms.

Future autonomous systems may require thermal sensing deployment at volumes that are orders of magnitude larger than historical thermal imaging programs. This creates enormous pressure on manufacturability, semiconductor scalability, packaging, calibration infrastructure, yield optimization, power consumption, and supply chain resilience.

Owl’s architecture and roadmap were built with these realities in mind.

We believe future leaders in thermal perception will not simply be companies with strong sensor performance. They will be companies capable of delivering manufacturable, scalable, semiconductor-driven thermal architectures aligned with modern autonomous system deployment requirements.

How does Owl fit into the broader autonomous defense trend?

The defense community increasingly recognizes that autonomous systems will play a central role in future force structures.

That includes:
– Autonomous drones
– Counter-UAS systems
– Autonomous ground systems
– Perimeter security
– ISR platforms
– Soldier-borne systems
– Robotic mobility platforms

Thermal perception is uniquely important in these environments because it provides robust detection capability independent of visible lighting conditions and performs exceptionally well against living targets and heat signatures.

At the same time, these platforms demand extremely aggressive SWaP-C requirements and increasingly require scalable deployment economics.

Owl is currently preparing to enter execution on a major U.S. government program focused on advanced next-generation thermal imaging technologies for autonomous systems. While we cannot discuss program specifics publicly at this time, the effort represents an important validation of Owl’s architecture, manufacturability strategy, and long-term vision.

We believe this is part of a much broader transition toward digitally architected thermal perception systems designed specifically for scalable autonomy applications.

What markets does Owl address today?

Owl operates across both commercial and defense autonomy markets.

These include:
– Autonomous drones and UAS
– Robotic mobility
– Defense ISR systems
– Perimeter security
– Industrial autonomy
– Automotive safety systems
– Commercial autonomous mobility

We view the market opportunity through a dual-use lens.

Many of the same core technical requirements exist across both defense and commercial autonomy:
– Reliable operation day/night
– Robust degraded visual environment performance
– Low power consumption
– Compact form factors
– AI-compatible outputs
– Scalable production economics

The convergence of these requirements creates strong long-term alignment between defense autonomy and commercial autonomy markets.

What is next for Owl Autonomous Imaging?

Owl has grown significantly over the last several years.

We now have active customer engagements across multiple autonomy and defense sectors, expanding strategic relationships across the semiconductor and thermal imaging ecosystem, and increasing focus on scalable deployment opportunities.

Our immediate priorities are:
– Executing on our next-generation thermal imaging roadmap
– Scaling manufacturing readiness
– Expanding strategic customer engagements
– Advancing deployable AI perception capabilities
– Supporting next-generation autonomous system requirements

We believe thermal perception is moving from a niche sensing modality into a foundational enabling technology for autonomous systems operating in the real world.

Future autonomous systems will require sensor diversity, robust perception under degraded conditions, scalable deployment economics, and manufacturable architectures capable of supporting very large deployment volumes.

That is the future Owl is building toward.

 

Contact Owl Autonomous Imaging

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