SemiWiki – Page 212 – The Open Forum for Semiconductor Professionals

March 21, 2022April 2, 2022

Alphawave IP and the Evolution of the ASIC Business

Alphawave IP and the Evolution of the ASIC Business
by Daniel Nenni on 03-21-2022 at 6:00 am
Categories: Alphawave Semi, IP, OpenFive

Alphawave IP has agreed to acquire OpenFive, a SiFive business unit (formerly Open-Silicon) for $210m in cash. Having spent many years in the ASIC business which included working with Open-Silicon, Alphawave, and OpenFive here is my perspective on the acquisition:

This acquisition accomplishes two things: First it trims down SiFive as they march to IPO. In concert with this acquisition SiFive raised an additional $175M which earned them a more than $2.5B valuation, doble unicorn status, which is a first for a semiconductor IP company.

Last year it was rumored that SiFive was in acquisition discussions with Intel, which I can confirm, but the valuation was too high. Intel CEO Pat Gelsinger has a strong acquisition background and has many opportunities. He also passed on Globalfoundries for the much smaller Tower Semiconductor which I think was an excellent move for both companies. GF’s IPO is booming and Israel based Tower Semi is a perfect fit to run the Intel Foundry business. The same goes for this acquisition. SiFive will successfully IPO and Alphawave IP will do quite well with the ASIC experts at OpenFive. This is one of the rare 1+1 = 3 semiconductor acquisitions, absolutely.

With OpenFive, Alphawave now competes in the multibillion dollar ASIC business with the likes of Marvel, who acquired the ASIC business from GF and eSilicon, and Broadcom who has the Avago/LSI Logic ASIC business.

You will also see Alphawave come out with standard products (my opinion) like Marvel and Broadcom putting them in the chip big leagues. Thanks to OpenFive, Alphawave expects to hit $500M in 2024 and I expect them to hit $1B not long after that. Yes, this acquisition is that good and I am sure there are more acquisitions to follow.

Alphawave and OpenFive did a much more detailed press release than we usually see for events like this so it is definitely worth a read. Here is the link to the PDF and some highlights:

This acquisition will nearly double the number of connectivity-focused IPs available to Alphawave customers from 80 to over 155 and will provide customers with a one-stop-shop for their bundled connectivity needs in the most advanced technologies at 5nm, 4nm, 3nm and beyond. This will include an expanded die-to-die connectivity portfolio that will accelerate chiplet delivery capabilities to customers. Alphawave has also licensed RISC-V processor IPs from SiFive as part of the transaction.
OpenFive’s proven silicon development team enables Alphawave to offer leading edge data centre and networking custom silicon solutions as well as enhancing its chiplet design capabilities. This accelerates Alphawave’s strategic goal to scale revenues by monetising its leading connectivity IP not only through IP licensing but advanced custom silicon design.
The combination of Alphawave’s leading high-speed connectivity with OpenFive’s IP portfolio is expected to generate material revenue synergies through bundling of IP and integrated IP sub-systems as well as leveraging the two companies’ respective strengths to win complex custom silicon design wins at leading edge process nodes.
The transaction will be immediately EPS accretive to Alphawave. Forecast FY 2023 revenue for the combined group is anticipated to reach between US$325m to US$360m with a path to a yearly revenue run rate of over US$500m in 2024. 2023 adjusted EBITDA margins for the group are expected to be between 32-36% with 2025 adjusted EBITDA margins between 40-45% as revenues exceed US$500m.

My good friend Paul McLellan and I wrote up the history of the ASIC business in our book “Fabless: The Transformation of the Semiconductor Industry”. Chapter number two “The ASIC Business” includes a brief history of two pioneering companies VLSI Technology (now part of NXP) and eSilicon (now part of Marvell). It is interesting to note that like many semiconductor market segments the ASIC business has come full circle and will boom again. But instead of the fabless transformation powering the ASIC business it will be domain specific chips by system companies, absolutely.

Also read:

Demand for High Speed Drives 200G Modulation Standards

The Path to 200 Gbps Serial Links

Enabling Next Generation Silicon In Package Products

March 20, 2022April 2, 2022

No Traffic at the Crossroads

No Traffic at the Crossroads
by Roger C. Lanctot on 03-20-2022 at 10:00 am
Categories: Automotive

The Federal Highway Administration in the U.S. tells us that “each year roughly one–quarter of all traffic fatalities and about one–half of all traffic injuries in the United States are attributed to intersections.” Intersections are clearly a challenge for human drivers, and the dirty little automotive industry secret is that intersections are an even bigger problem for computer-driven vehicles.

While humans run red lights – resulting in 700-800 fatalities annually – computer driven cars struggle to accurately identify the presence of traffic signals and stop signs and proceed appropriately without human assistance. Intersections are the great Achilles heel of autonomous driving – aside from all of the other weaknesses and unsolved problems these vehicles face – and remain an enormous challenge for transportation engineers.

One company, NoTraffic, has brought new thinking and new technology to municipalities. The two key insights that NoTraffic has employed are:

Developing proper metrics for categorizing and quantifying the types of crashes that occur at intersections – left turn, rear-end, red-light running, right turn?
Providing tools for communicating with drivers who are approaching intersections to alert them to the signal phase and timing of the light AND the potential for pedestrians in the crosswalk or a red light runner?

We hear a lot about connected cars these days. What we don’t hear much about is connected traffic lights and infrastructure. This is where NoTraffic comes in.

NoTraffic takes traditional disconnected infrastructure solutions – manifest in all those road-side boxes visible near traffic light installations – and digitizes them thereby allowing them to become part of a managed and always connected grid.

Nowhere is this transformation and its impact clearer than in NoTraffic’s approach to red-light runner mitigation. Rather than implementing enforcement cameras and the corresponding terror, confusion, and anger of impacted drivers, NoTraffic takes a more sophisticated, calibrated approach.

NoTraffic starts with measuring the extent of the problem. You can’t mitigate what you can’t measure.

NoTraffic breaks red-light runners into three “tiers:”

Tier 1: A vehicle in the intersection during the yellow phase (light) with no conflicting green.
Tier 2: A vehicle in the intersection during the red light, yet the intersection is in an all-red interval (no one has a green light).
Tier 3 (most dangerous): A vehicle in the intersection during the red light and there is a conflicting green

Significantly, NoTraffic both measures and mitigates the problem – identifying troublesome times of the day when spikes in red-light running tend to occur – and then identifying solutions that typically do not require red light enforcement cameras.

Based on cameras plus Wi-Fi and cellular connectivity along with cloud-based analytics, NoTraffic’s solution provides three layers of safety benefits while also leveraging existing installations:

A grid-level view into dangerous intersections, on a real-time basis, allowing cities to take precise measures – i.e. deploy local police to deter red-light runners at specific intersections and hours of the day (for example, intersections 1, 3, and 5, during rush hour traffic).
Reduction in the potential for red-light-running crashes: by shortening delay times and queue lengths – in the example illustrated above, NoTraffic reduced the number of red-light runners, potentially minimizing the number of life-threatening crashes.
Real-time notifications can be sent to road users by connecting urban intersections to a managed grid via their V2X-enabled IoT sensors, thereby enabling alerts to be sent to road users – connected vehicles, pedestrians, or cyclists – warning of vehicles about to cross an intersection on red, thus potentially minimizing the number of life-threatening crashes.

The data in the chart (above) represents a two-week window, gathered in April and May 2021 (two weeks in each month) in a major U.S. city where NoTraffic was deployed along a 1.8-mile corridor across five intersections. One of the intersections shows a slight increase during the mitigation period, which might be “noise” or lower cycle length for the reversible lane.

Most important of all, NoTraffic’s connected infrastructure solution is capable of communicating via cellular wireless technology with approaching vehicles or nearby pedestrians to warn of an identified red-light runner.

This is the future of connected infrastructure – with intersections connected to one another and to approaching vehicles and pedestrians. NoTraffic is more than a little ahead of its time – or maybe it’s right on time. The NoTraffic solution offers the promise of reducing the number of red-light runners or at least warning drivers and pedestrians when they occur. Perhaps just as important, the NoTraffic approach will lend a helping hand to hopeless autonomous vehicles.

Also read:

GM’s Super Duper Cruise

Emergency Response Getting Sexy

Waymo Collides with Transparency

March 20, 2022April 2, 2022

Facebook or Meta: Change the Head Coach

Facebook or Meta: Change the Head Coach
by Ahmed Banafa on 03-20-2022 at 6:00 am
Categories: General

The title of this article shows one side of the problem with #Meta or Facebook which is how people saying the name and adding “whatever their name now….”, but let me get down to the main points by giving this example of comparing Facebook changing of its name to Meta, to repainting an old house with cracks and outdated design which will not make it new, the minute people look carefully or walk through they know its old.

This is the story of Facebook (I will use this name as it’s the real well-known brand name for years, forget about Meta for now) I am also comparing #Facebook business to an NFL team with bad news after bad news from losing daily visitors, shutting down cryptocurrency idea, announcing defeat by TikTok and losing the last ounces of users trust with the whistleblower in 2021 , keeping all that in mind it’s time to change the head coach and I mean both Mark Zuckerberg and Sheryl Sandberg , they ran their course , no more new tricks, magic is gone and Facebook needs new faces and new direction, you can’t do the same and expect different results (yeah! there is a name for that action).

Let’s see how Facebook can be saved:

First, Mark Zuckerberg is hitting his 18th year of running the company, time for him to step down like many other founders/CEOs of top Silicon Valley companies. Instead of trying new products Facebook should try a new CEO and both Mark and Sheryl they can enjoy their time doing something else besides running the company’s daily business.

Second, spin off REELS (the underperforming competitor of TikTok) I checked both and the differences are clear, #TikTok is a happy place with all the actions, filters and funny videos, while Reels is a strip down version of Instagram, no excitements, no vibes, and it’s still part of Instagram, not to mention the confusing upload steps of a video. So, as I said, spin it off and change the name to something Gen-Z and Millennials will love to talk about.

Third, take the Metaverse as a separate company away from Facebook and Mark can run it and experiment with it but away from social media business (Facebook, Instagram, Whatsapp, Messenger) like someone who try new idea in the lab, if it fails like cryptocurrency no impact on the other parts of the business, and no bad reputation. It is a very expensive experiment but if that’s what will take Mark away from other parts of the company it’s worth it.

It’s 2022 and the tricks and techniques of the past will not work now, this is a new era with generations who see their lives through Apps that define their personality and open opportunities for them not through vague terms like #Metaverse that will take place years in the future.

Also, instead of invasion of privacy at each turn when dealing with Facebook products, Facebook’s new management should try to be honest and clear with users about their data. Apple figured it out that “Privacy” is the key to block competition and gain customers, TikTok talked to young people and gained access to their mindsets, but Facebook still served drinks and foods from the early 2000’s.

Let me say this one more time, Mark Zuckerberg was the driving force for Facebook. Now he is the brakes that keep holding back any progress of the company. Ten years ago, no one could imagine Facebook without Mark, now many wanted to envision the company without him so the company can do better.

Ahmed Banafa, Author the Books:

Secure and Smart Internet of Things (IoT) Using Blockchain and AI

Blockchain Technology and Applications

Quantum Computing

Podcast EP67: Corigine Combines Emulation and Prototyping

Podcast EP67: Corigine Combines Emulation and Prototyping
by Daniel Nenni on 03-18-2022 at 10:00 am

Dan is joined by Jeff Critten, VP of sales at Corigine. They discuss the unique capabilities of Corigine that allows support of both emulation and prototyping in one platform.

Jeff Critten has been in the EDA industy for over 25yrs. He started with Cadence as a verification AE in 1997 and moved into a sales role where he was promoted to a Sales Director running Major Accounts like Intel, Broadcom, Marvell and numerous others. He left Cadence to try a Cloud start up and that gamble didn’t bear fruit. He recently went to Corigine in Q4 to become their VP of Sales for their new Mimic Product line which unifies the functions of an emulator and a prototyping board into a single unifed platform that is priced like a prototyping board. Jeff has a MSc. from the University of Waterloo and his BSc from the University of British Columbia.

The views, thoughts, and opinions expressed in these podcasts belong solely to the speaker, and not to the speaker’s employer, organization, committee or any other group or individual.

March 18, 2022April 2, 2022

CEO Interview: Aki Fujimura of D2S

CEO Interview: Aki Fujimura of D2S
by Daniel Nenni on 03-18-2022 at 6:00 am
Categories: CEO Interviews, ESD Alliance

Curvilinear Design Primer for Design, Packaging Communities

This interview was done by Bob Smith, Executive Director, ESD Alliance, a SEMI Technology Community.

Previously, Fujimura served as CTO at Cadence Design Systems and returned to Cadence for the second time through the acquisition of Simplex Solutions where he was President/COO and inside board member. He was also an inside board member and VP at Pure Software. Simplex and Pure both went public during his tenure. Fujimura was a founding member of Tangent Systems, subsequently acquired by Cadence Design Systems. He was a board member of HLDS, RTime, Bristol, S7, and Coverity, Inc., all of which were successfully acquired. Fujimura received Bachelor of Science and Master of Science degrees in Electrical Engineering from MIT.

Semiconductor packaging and photomask segments of our industry have undergone some major technology changes in the past few years after relatively minor changes for many years. In the case of photomasks, new technologies such as multi-beam mask writers and extreme ultraviolet (EUV) lithography are major breakthroughs in the news as they ramp into high-volume manufacturing. A new trend related to these technologies is the use of curvilinear features on photomasks.

Curvilinear photomasks are here today, particularly interesting to the ESD Alliance as the door opens for “curvy” design. Aki Fujimura, CEO of D2S and a member of the the ESD Alliance Governing Council, speaks to me about curvilinear photomasks and what it means for design and packaging.

BS: What are the advantages of curvilinear photomasks?

AF: First let me explain what we mean by curvilinear photomasks. Shapes consisting of axis-parallel edges are sometimes referred to as Manhattan geometries. Shapes that do not need to be Manhattan geometries are considered curvilinear in the context of our discussion.

Curvilinear mask features have been shown not only to print more accurately, mostly because 90-degree corners can’t be accurately reproduced, but also to print more reliably, with less variation. This is good for both mask and wafer quality.

BS: What breakthroughs enabled curvilinear photomasks?

AF: Multi-beam mask writing and GPU-acceleration of pixel-based computing including curvilinear inverse lithography technology (ILT) are enabling curvilinear masks. With multi-beam mask writers available in all leading-edge mask shops now, the mask write times are no longer affected by the number of shapes on the mask or their complexity. This is principally because multi-beam mask writers write with pixels, similarly to how TVs, monitors, and digital projection machines work.

The economics of mask writing is dominated by the mask writing time. The fact that multi-beam mask writers, given a resist and writing method, writes any shapes of any shape count in constant time is economically and logistically very attractive to the mask shop. Once a mask shop has a multi-beam mask writer, curvilinear masks take no more time to write than any other.

BS: What is ILT and how does it contribute?

AK: ILT is a mathematically rigorous inverse version of optical proximity correction (OPC) known to produce the best wafer results for both optical (193i) and EUV lithography. Many studies have demonstrated that curvilinear ILT mask shapes produce the best “process windows,” a measure of resilience to manufacturing variation.

Until multi-beam mask writers became available in the leading-edge mask shops, it hadn’t been practically possible to use curvilinear mask shapes as the desired mask shapes provided to the mask writers. However, runtimes associated with this computational technique limited its practical application to critical “hotspots” on chips.

Applying GPU acceleration to the ILT problem paved the way in the past few years for some breakthroughs in runtime roadblocks to ILT. In 2019, an entirely new approach systematically designed for multi-beam mask writers and GPU acceleration by D2S made full-chip ILT a practical reality in production for the first time.

BS: Will curvilinear masks be used for 193i lithography, EUV or both?

AF: In annual surveys conducted by the eBeam Initiative (See Figure 1), industry experts anticipate that curvilinear ILT shapes are already in use or will be for hotspots in some leading-edge layers before 2023 for both 193i and EUV masks. They clearly indicate that the primary reason to purchase multi-beam mask writers is for EUV masks. They also indicate that writing curvilinear masks is also a strong reason to purchase multi-beam mask writers.

Given that EUV masks are being written with multi-beam mask writers already, there is no penalty in the mask write time to write curvilinear shapes. Whether for 193i or for EUV, curvilinear mask shapes produce better wafer quality. With sufficient supply of multi-beam writers, leading-edge masks are likely to be written with them in the future.

Figure 1 caption: 2020 eBeam Initiative Survey result in answer to the question: “How extensively will curvilinear shapes be used for leading-edge (EUV, 193i) masks intended for high-volume manufacturing (HVM) by 2023?” (a) 94% believe curvilinear shapes will be used for 193i for HVM by 2023, (b) 85% expect that EUV also needs curvilinear shapes for HVM.

Source: eBeam Initiative

BS: Where is the industry in terms of adoption of curvilinear photomasks?

AF: With multi-beam mask writing being widely available for the leading-edge nodes, manufacturing curvilinear ILT shapes is now possible.

And the rest of the mask making infrastructure shown in Figure 2? A limited number of curvilinear shapes can already be handled by leading-edge mask shops today, according to leading authorities. For wide-spread use, there are likely more streamlined solutions needed for metrology, inspection, dispositioning and repair.

Figure 2 caption: A typical photomask manufacturing flow follows a specific pattern.

Source: D2S

BS: How do curvilinear photomasks unlock new opportunities for design?

AF: As we anticipate this exciting transition to curvilinear mask making or “curvy” design, an upstream effect of this change is being studied by some. Figure 3(a) below shows an image from an Imec paper in 2019 that highlighted potential improvements in compacting cell designs, decreasing load, and decreasing interconnect delay through the use of curvy design. Figure 3(b) from a Micron presentation illustrates the use of manual manipulation to jog multi-bit busses using non-Manhattan, curvilinear shapes of varying angles. Manual manipulation is resource intensive, a clear indication of the benefits being significant enough to be worth the trouble, at least for a memory maker. The entire chip design infrastructure is based on the Manhattan assumption.

In my previous life in EDA, I had something to do with that, so I know this very well and it is not going to change any time soon. At the same time, though, is there any doubt that a curvilinear chip, if magically made possible, would be smaller, faster, and use less power?

Figure 3 caption: (a) An Imec paper showing “curvy” designs are feasible with the reliable manufacturing of curvy masks, (b) an example wafer image from Micron with non-Manhattan design.

Source: D2S

Also Read

CEO Interview: Frankwell Lin, Chairman and CEO of Andes Technology

CEO Interview: Tamas Olaszi of Jade Design Automation

CEO Interview: John Mortensen of Comcores

March 17, 2022July 18, 2022

5G and Aircraft Safety: Simulation is Key to Ensuring Passenger Safety – Part 4

5G and Aircraft Safety: Simulation is Key to Ensuring Passenger Safety – Part 4
by Shawn Carpenter on 03-17-2022 at 10:00 am
Categories: 5G, Ansys, Inc., EDA

In our previous blog installments, we examined the ingredient for modeling the potential for interference between a 5G C-band base station and an aviation radar altimeter. Using candidate emissions models for the transmitter, wideband susceptibility models for a candidate radar altimeter receiver and antenna and propagation models for the wireless channel, we arrived at an analysis for a worst-case static arrangement of the systems. In addition, we explored a potential interference mitigation technique to eliminate the in-channel interference experienced by the radar altimeter which involved the design of a low-pass filter for the 5G base station.

Next, we add the component of real-world motion.

Dynamic Interference Assessment: Interference Simulation for an Airport Approach

To better understand the way interference might occur during a landing, takeoff, or go-around, we need to simulate the scenario as it unfolds during the flight process. This requires simulating the interference situation during the flight sequence in an accurate virtual environment involving a particular runway of interest.

Variations in the flight path and aircraft dynamics should be considered to determine worst-case scenarios for interference situations. These could include aircraft roll during landing due to wind gusts and turbulence, which could rotate the aircraft radar altimeter antenna to stare into a nearby 5G C-band base station on the ground. They could also include the impact of low-height base stations and propagation interactions with buildings and structures near the ground and around the airport.

Exploring these cases through experimental flight will be extremely costly and require control of the airspace and the electromagnetic spectrum around the airport during testing. Repeating these experiments for each of the variables listed above is simply untenable. But with modeling and simulation, we can explore these scenarios virtually and automatically, yielding the top scenarios that may warrant a final flight test for measurement-based validation. In fact without simulation, it is unrealistic to expect that all scenarios could be checked out experimentally within six months.

We have assembled such a simulation for a landing scenario near King County International Airport in Seattle, Washington. The figure below displays a landing scenario set up in the Ansys AGI Systems Tool Kit (STK) software. A notional long-range, wide-body aircraft is shown with the antenna pattern for an installed radar altimeter system. The landing trajectory is shown by the blue line on a south-by-southeast heading, which includes a taxi distance on the runway after landing. A 5G C-band base station antenna system is indicated directly under the flight path, with a mounting height of 9.5m, at or below roofline height of nearby buildings. AGI STK includes local terrain in scenarios, and even Mt. Rainier is visible in the distance.

Figure 11 – Landing approach scenario in AGI STK for an aircraft at King County International Airport in Seattle, WA. Simulated approach includes time-indexed flight dynamics, including aircraft pitch and roll effects on radar altimeter antenna pointing.

The aircraft will pass quite close to the 5G C-band base station in this scenario as shown in the figure below, but we have the freedom to place and move our base station antennas anywhere we wish, enabling rapid re-evaluation of the scenario.

Figure 12 – STK simulation shows landing geometry as aircraft passes close to the 5G C-band base station in the scenario. Projection of the radar altimeter gain contours can be seen on the ground beneath the aircraft.

The radios used in this simulation are identical to those defined in the static interference analysis, with the notable exception that the out of band saturation power level for our radar altimeter receiver is -30 dBm. It should be noted that this does not reflect the actual radar altimeter system installed on a particular aircraft, but is simply a notional system design based on the range of radar altimeter systems presented in the RTCA report to the FAA dated Oct. 2020.

Antenna-to-Antenna Coupling Captured with Physics

An important addition to this simulation is the use of high-fidelity physics simulation in computing the antenna to antenna coupling from moment to moment during the scenario. Recall that each antenna pattern has its basis in an electromagnetic simulation by Ansys HFSS and HFSS SBR+ to capture installed radar altimeter antenna effects as well as to capture an accurate radiation pattern for the 5G C-band phased array antenna.

The antennas are set into a model of the larger scattering environment that includes tower, buildings and large scattering structures around the airport, and the antenna-to-antenna coupling is sampled by HFSS SBR+. With this solution approach, potential masking and multi-path reflections by nearby buildings and structures are included in the physical path coupling from C-band 5G base station to radar altimeter antenna. S-parameter coupling data can be computed for a single frequency or a high number of frequencies sampled across any band of interest.

Interference Scenarios for the Current and Future 5G C-band Channel Implementations

The video below shows the complete landing scenario as simulated. In the inset graph, the electromagnetic interference (EMI) margin is illustrated. EMI margin represents the interfering transmitter spectral power present across the band of interest in the radar altimeter receiver front end, minus the receiver’s ability to reject that power. When the EMI margin (black curve) rises above the red line, the potential for interference exists and the receiver is either saturated (by a strong out of band signal) or de-sensitized (by a strong in-band signal). In addition, the EMI margin legend on the plot is color-coded to signify interference at any time. Green indicates an interference-free operation in the band, blue and yellow indicate EMI margins that have crossed indicator thresholds near interference conditions, and red indicates an interference event is occurring. The following simulation is conducted with a 5G C-band system operating in the current band of 3.7-3.8 GHz:

We can see strong interference occurs as the aircraft passes over the 5G C-band tower. The radar altimeter registers interference within its operational channel (centered at 4.2 GHz), and the receiver is also saturated from the strong 5G signals that are outside of the radar altimeter’s intended band of operation.

We can easily change the 5G C-band transmitter definition in our simulation to consider interference potential when the telecom operator uses the 80 MHz band from 3.9 to 3.98 GHz. Because it is closer to the radar altimeter band, we might expect that the potential for interference to the radar altimeter to be enhanced, and a quick re-simulation reveals this to be the case:

How to Fix to 5G C-Band Airport Issues

With modeling and simulation within scenario modeling, we can explore any radar altimeter, on any aircraft, at any airport runway, against any C-band 5G service towers that exist within a given radius of any airport. Given models of sufficient fidelity, this can all be done on a computer by individuals at any location without requiring flight time or impacting airport operations. Using simulation, we could:

Explore or modify existing or planned radar altimeter systems
Explore or modify 5G C-band base station performance parameters
Examine cases where multiple 5G C-band base stations might exist
Examine edge cases with respect to aircraft flight landing/takeoff dynamics (roll/pitch) which could result in antennas looking into one another’s high-gain zones
Examine and explore reasonable limits on power, beams teering, effective isotropic radiated power (EIRP), and modifying 5G service tower keep-out zones around airports
Provide guidance on flight planning for helicopters, private aircraft, delivery drones and more

Interference between the adjacent C-Band 5G spectrum services and radar altimeter systems is both predictable and solvable. Given sufficient fidelity in the underlying models, simulation represents a cost- and time-effective way to unobtrusively test and validate potential interference scenarios for any aircraft at any runway location. Scenarios can go beyond considering just 5G towers near the airport — with tools like AGI STK and Ansys Electronics Desktop we can look at any combination of wireless systems that might exhibit interference potential. This could become a key enabler for functions like low-altitude flight planning for helicopters, urban air mobility, drone delivery systems, and more.

Also read:

5G and Aircraft Safety: Simulation is Key to Ensuring Passenger Safety

5G and Aircraft Safety: Simulation is Key to Ensuring Passenger Safety – Part 2

5G and Aircraft Safety: Simulation is Key to Ensuring Passenger Safety – Part 3

March 17, 2022April 2, 2022

Balancing Test Requirements with SOC Security

Balancing Test Requirements with SOC Security
by Tom Simon on 03-17-2022 at 6:00 am
Categories: EDA, Siemens EDA

Typically, there is an existential rift between the on-chip access requirements for test and the need for security in SoCs. Using traditional deterministic scan techniques has meant opening up full read and write access to the flops in a design through the scan chains. Having this kind of access easily defeats the best designed functional mode security. With so many new applications requiring security, it is essential to eliminate any security holes created because of test.

Furthermore, test has moved from being something done only during manufacture, to an ongoing requirement to monitor chip operation. New approaches are needed to fulfill expanding needs and at the same time close down any avenues that might compromise chip security. Siemens EDA has written a white paper that outlines how a wide range of techniques can be utilized separately or together to provide security in depth. Interestingly many of these techniques are already extremely desirable from a strictly test perspective.

The white paper titled “High-quality test and embedded analytic solutions for secure applications” written by Lee Harrison talks about the need for secure test and illustrates a series of techniques that are very useful. At the core of each of these techniques is the notion that there should be no direct connection between the external test pins and the internal scan chain. Without this separation the chip can be probed and even controlled with ease. Fortunately, many of the innovations for test to meet speed, capacity and flexibility requirements have the added benefit of abstracting access to the scan chains themselves.

The Siemens white paper describes a progression of techniques, starting with logic built-in self test (LBIST) that secure the scan mechanisms on SoCs. The benefits of LBIST are multifold, offering in system test, speeding up test time and comes with added benefit that it seals off the scan chain from direct external access. However, LBIST without a BIST bypass mode can limit the ability to diagnose failures. So additional methods such as adding security features to the test access point (TAP) controllers may be needed.

Test compression is another effective way to limit direct access to the scan chains. Conveniently it also is useful for other directly related test improvements, such as reducing tester data transfer volume. Tessent TestKompress is already widely used for testing chips for smart cards and defense related designs where it adds a layer of security. Test compression screens both the input and output of the scan chains.

Further isolation and encapsulation of the scan chains is achieved through the Tessent Streaming Scan Network (SSN). Here all of the scan data is packetized and transmitted through a dedicated on-chip test network. Of course, as above, there are many practical benefits and reasons to deploy an SSN. It offers full isolation of the scan chains which can only be accessed electrically by the SSN nodes embedded within the chip.

Secure Test for SOCs

The Siemens white paper provides insights into several other methods that can improve security while meeting the needs of testability. Foremost among these is upgrading the interface to the TAP controller so that it is in a safety island. This safety island can use a state-of-the-art security manager to limit access based on security needs. It also makes it possible to offer different levels of secure access depending on whether the chip is still on-premise during manufacture or deployed in the field as part of a system.

The white paper makes good reading and offers useful information for anyone looking to tighten up security through this potentially risky portion of their design. The white paper can be downloaded here through the Siemens website.

Also read:

Siemens EDA on the Best Verification Strategy

Scalable Verification Solutions at Siemens EDA

Power Analysis in Advanced SoCs. A Siemens EDA Perspective

March 16, 2022July 18, 2022

5G and Aircraft Safety: Simulation is Key to Ensuring Passenger Safety – Part 3

5G and Aircraft Safety: Simulation is Key to Ensuring Passenger Safety – Part 3
by Shawn Carpenter on 03-16-2022 at 10:00 am
Categories: 5G, Ansys, Inc., EDA

In our previous blog installment, we introduced the components of RF interference modeling, and established models for wideband peak emissions of a 5G C-band transmitter and the wideband receiver susceptibility for a radar altimeter receiver. Here, we consider the third component: the wireless channel, which considers the physics of the antennas and how they couple energy in the environment. After we establish parameters for the wireless channel, we will be in a position to combine all three to get an accurate estimate of the maximum interference potential at any frequency.

Antenna-to-Antenna Coupling

Modeling interference accurately depends upon having accurate models for coupling the power coming out of the transmitter to the receiver — across the entire band. This is important for both in-channel coupling as well as out-of-band emissions coupling. Measurement of antenna coupling is challenging because of the spacing between the antennas and the fact that the radar altimeter antenna is in continual motion.

The wireless channel of Figure 4 (in the previous blog entry) consists of the 5G transmit antenna and its characteristics for focusing power in a given direction, the radar altimeter antenna and its directional gain sensitivity, and the propagation (and loss) of the signal as it travels between the two antennas. For accurate antenna performance, Ansys HFSS can be used to accurately predict the antenna behavior through electromagnetic simulation, to capture beamforming performance and the way the antenna’s host platform interaction modifies the antenna’s performance. Figure 6 shows a notional dual band 5G antenna unit and a candidate radar altimeter antenna design under an airliner airframe as simulated by HFSS and HFSS SBR+.

Figure 7 – Ansys HFSS models use electromagnetic physics to simulate the antenna radiation characteristics for a notional 5G radio antenna (left) and an installed radar altimeter antenna on a large commercial aircraft

As you can observe from the patterns shown, the antennas can direct energy in very specific directions. Therefore, the beam steering control of the 5G antenna will be important, as will the location and orientation of the aircraft during landing and takeoff. If the aircraft rolls during these phases of the flight due to turbulence or other actions, the antenna’s sensitivity region will roll with it.

Finally, the link between the antennas must be accurately determined, and this can be computed using standard propagation loss formulas or by using an electromagnetic analysis solution like HFSS SBR+. For our purposes here, we will use a propagation loss model. Ansys EMIT can also include the effects of water vapor, rain, rain rate, and fade effects if desired. Because these effects would only introduce additional losses which would reduce interference, we’ll leave them out for now.

With EMIT, the antenna characteristics and the wireless propagation between them is simulated at all frequencies and used for the chain calculation depicted in Figure 4.

Static Interference Assessment: Putting the Ingredients Together

We would like to conduct a test to see whether either in-band interference or out-of-band interference could be experienced by the radar altimeter due to a 5G transmitter near the airport, for an assumed worst-case static placement of the RF systems involved. This involves an analysis using worst-case coupling between the systems, as well as reasonable candidate designs for the 5G transmitter and radar altimeter receiver. We need a few more details to round out the scenario.

Distance from 5G base station to airport runway approach	400 m
Height of 5G base station	40 m
Base station antenna gain	22 dBi (pointed at aircraft)
Radar altimeter antenna gain	11 dBi (aircraft rolling, pointing at 5G base station)
Aircraft altitude	100 m

This represents a worst case, representing a base station with high power, focusing a beam at the landing aircraft, which is rolling in such a way as to place the peak of the radar altimeter radiation pattern on the base station. However, when setting standards, or studying critical keep-out zones for radiating towers, this is the type of analysis that one needs to use. Any of the parameters in this analysis can be changed at any time to quickly assess interference mitigation strategies.

Let’s examine the results for the initial C-Band service rollout in the 100 MHz band from 3.7-3.8 GHz. Figure 8 shows the result of our investigation. The black curve gives us a view of what is going on in the receiver and measures the difference between the transmitted power at each frequency and the receiver’s ability to reject that energy (receiver susceptibility). If this value goes above zero (the red line), we have an interference event because the receiver can’t reject that energy at that frequency. We can also place threshold values to watch for frequencies where we are getting close to an interference event. The plot suggests that the 5G transmitter out-of-band emissions are creating strong interference potential (for our environment conditions) within the receive band of the radar altimeter. The in-band radiation (3.7-3.8 GHz) of the 5G transmitter is close, but not exceeding the receiver saturation so this is not causing interference.

Figure 8 – EMI margin analysis for the current C-Band service implementation for our sample scenario. The out-of-band emissions from the 5G base station causes in-band interference to the radar altimeter antenna in regions where the black curve exceeds the red line. 5G emissions will need to be reduced by at least 15.3 dB to mitigate the interference.

The service providers have spent a great deal of money on all three of these channels, and eventually will want to enable service on the additional 180 MHz contained in the two bands above the current operational band. What happens when these bands are enabled in the future against this radar altimeter in our worst-case scenario?

Figure 9 shows that we face basically the same problem for the next 100 MHz band (3.8-3.9 GHz). However, the plot on the right shows that a new problem crops up if the last 80 MHz band (3.9-3.98 GHz) is activated. The interference appears to be due to the 5G emissions mask putting higher power levels into a part of the spectrum where the altimeter receiver has reduced rejection, and strong interference exists here which will require at least an additional 25 dB of 5G signal reduction over the lower two channels to ensure coexistence.

Figure 9 – EMI margin analysis for the future C-Band channels (3.8-3.9 GHz on left, and 3.9-3.98 GHz on right) for our sample scenario. The out-of-band emissions from the 5G base station causes in-band interference to the radar altimeter antenna in regions where the black curve exceeds the red line. In-band interference potential is shown for the 3.8-3.9 GHz channel, whereas very strong out-of-band interference in the radar altimeter receiver is expected to cause receiver saturation from the 3.9-3.98 GHz channel.

Ansys EMIT can be used to evaluate these mitigation strategies quickly — without requiring a single hour of flight time. For example, if we add a low-pass filter to the 5G transmitting elements (which we could easily design and synthesize using the Ansys Nuhertz FilterSolutions software), we could explore the impact of a filter on reducing the 5G system’s out of band emissions on the radar altimeter. With a low-pass filter added to the 5G transmitter chain in EMIT (1 dB of in-band loss, 40 dB of rejection above 4 GHz), we see an immediate improvement — the interference is eliminated. Figure 10 shows the EMI margin plot with the filter in place, showing that we have 5.2 dB of “head room” before interference occurs at any frequency.

Figure 10 – Adding a low-pass filter to the 5G base station transmitting elements has eliminated interference for the use of the 3.7-3.8 GHz 5G channel on the radar altimeter.

You may wonder whether we use simulation to examine (and validate) specific radar altimeters against specific 5G base station installations at specific airports. Interference potential is a dynamic phenomenon, and the situation changes from moment to moment as the aircraft lands or takes off. In our next blog installment, we’ll hook up this interference modeling machinery to our Ansys AGI STK flight simulation capabilities and show you what the interference looks like during a landing or takeoff when dynamic motion, position, and orientations are considered at a prospective airport setting.

Also read:

5G and Aircraft Safety: Simulation is Key to Ensuring Passenger Safety

5G and Aircraft Safety: Simulation is Key to Ensuring Passenger Safety – Part 2

5G and Aircraft Safety: Simulation is Key to Ensuring Passenger Safety – Part 4

March 16, 2022March 28, 2022

Siemens EDA on the Best Verification Strategy

Siemens EDA on the Best Verification Strategy
by Bernard Murphy on 03-16-2022 at 6:00 am
Categories: EDA, Siemens EDA
5 Comments

Harry Foster opened and wrapped a tutorial at DVCon 2022 on “The Best Verification Strategy You’ve Never Heard Of”. Harry started with a common refrain on verification; we face a crisis thanks to a combination of growing complexity in the systems we are able to design, yet double exponential growth in verification cost for those systems. He looks at this as an unintended consequence of separating verification from design around the 1990s.

This separation seems logical, but it leads to a problem Deming called out over half a century ago. Inevitably we gravitate towards trying to verify quality into the product rather than designing in and controlling quality from the outset. Which as Deming pointed out does not work well in any engineering context and is certainly not scalable. Harry suggests a different strategy based on design strongly coupled with intent focused insight all the way through the design lifecycle. He maps this onto 3 pillars: producing correct intent by construction, proving that the intent is met, protecting the intent throughout the design lifecycle. Harry also has whitepaper on this topic which is a good read.

Produce

The main point here is that the density of bugs per N lines of code is more or less independent of the application – video games, mobile apps, or hardware design. Aside from using pre-validated IP, the best knob to control total number of bugs in a design is to reduce the number of lines of code by coding in higher-level language (HLL).

Which naturally leads most of us to think of SystemC or C++. This section of the tutorial illustrates with an example HLL design for a digital pre-distortion block, compensating for non-linear behavior in the following power amplifier. They also cite a Google paper at HotChips on using HLL to build a video codec, for which Google asserts they found 99% of functional bugs before running any RTL simulations. A design in a high-level language will create less bugs and those bugs will emerge faster thanks to faster simulations.

Other signal processing functions derive similar value – communications, video and audio pipelines are common examples. The growing importance of all these functions highlights the benefits of this approach to SoC design in general. Of course, not all functions build on signal processing, but the principle of HLL still stands in my view, though in different domain-specific languages. For example, you might choose to implement a GPU in Chisel.

Prove

I’m a big believer in the message from this part of the tutorial – that before you run a minute of simulation, you should be running as much static analysis as you possible can. This section of the tutorial laid out a pretty detailed list, some familiar, some less so. This starts with Linting to find semantic, structural and stylistic problems. Then onto formally supported Linting to detect potential deadlocks in state machines, value overflow in assignments and similar issues. Then initializations and X-checking. Next, domain crossing checks for clocks and resets. Then design connectivity checks (did any top-level connections break in the last drop?). And register checks.

Then it gets more interesting, at least for me. Operational assertions, coded against the OneSpin TiDAL library, check functionality against specifications using formal methods. They apply their approach to trojan detection in which they can prove not only that a core does what it is supposed to do but also that it does not do anything it is not supposed to do. They cite a paper (not easy to find) presented at GOMAC in which they found a Trojan kill switch.

Static checks are also important in implementation, where logic correct at RTL can become incorrect in a gate level mapping. Or in FPGA implementation, where equivalence checking between implementation and RTL can be quite different from ASIC flows. Overall, what is important is that all these checks are static, amenable to relatively quick runtimes. This is critical in continuous integration disciplines. There, most potential failures must be flushed out quickly before longer simulation regressions start.

Protect

I confess the presentation on this topic confused me. Perfectly reasonable product pitch on the features and benefits of the Siemens EDA hardware accelerator line. But what did that have to do with Protect? I decided maybe it was so obvious to the presenter that it didn’t need explanation. I looked back at the earlier paper which helped a little with this statement:

Finally, the Protect pillar consists of analysis tools that ensure the intent of the design is retained throughout the entire development life cycle; for example, identifying new metastability issues potentially introduced during the synthesis and implementation process.

Maybe through continued regression , the strategy can continue to ensure that design intent stays on track. Sounds reasonable, but I would welcome some more explanation on the connection between Protect and these activities.

You can watch the recorded tutorial HERE.

Also read:

Scalable Verification Solutions at Siemens EDA

Power Analysis in Advanced SoCs. A Siemens EDA Perspective

Faster Time to RTL Simulation Using Incremental Build Flows

March 15, 2022July 18, 2022

5G and Aircraft Safety: Simulation is Key to Ensuring Passenger Safety – Part 2

5G and Aircraft Safety: Simulation is Key to Ensuring Passenger Safety – Part 2
by Shawn Carpenter on 03-15-2022 at 10:00 am
Categories: 5G, Ansys, Inc., EDA

In our first blog installment, we outlined the interference concerns surrounding the coexistence of the new C-band 5G telecom service spanning the band from 3.7 to 3.98 GHz with aviation radar altimeters. Radar altimeters are essential components for safety during landing and takeoff, as they offer precise measurements from the aircraft to the ground. For background on the spectrum allocation involved, please refer to our earlier installment.

We will now consider the components required in a high-fidelity interference analysis aimed at determining the maximum interference potential between a 5G C-band transmitter and a radar altimeter receiver.

The Anatomy of an Interference Analysis

The traditional method for determining whether interference exists has been to simply turn on the radios involved and measure the spectrum. In the case of 5G C-band interference with radar altimeters, this would involve turning on a tower near an airport, pushing peak traffic levels through the radio system, flying an aircraft through the airspace with a particular radar altimeter system, and taking many data samples. Undertaking real measurements is costly for many reasons:

Testing can only validate one radar altimeter at a time per test aircraft, and depending on antenna interaction with the host airframe, may only apply to one aircraft type at a time
Other signals within the 5G and radar altimeter band would need to be “quieted” so that measurements are not biased by contributions from other signals in the area
The airspace would need to be cleared of other aircraft while testing is conducted
Testing would apply to one 5G base station location at a time, and one airport at a time

These are just some factors that lead to a very high cost of validation through measurement.

With sufficient fidelity, simulation offers a very cost-effective and repeatable way to test and validate combinations of radar altimeters, host aircraft, C-Band 5G base station combinations and parameters, and airport locations. Let’s examine a worst-case interference analysis via simulation. In our case, we will use the Ansys Electronics Desktop, featuring the Ansys HFSS simulator for modeling antennas and their interactions with their local environment, and the Ansys Electromagnetic Interference Toolkit (EMIT) for modeling wideband interference potential between radio systems.

Both in-channel and out-of-band effects are considered. Beyond transmitters and receivers, the antenna systems must also be considered, allowing for the orientation and position of the aircraft and for the beamforming and beam steering characteristics of the 5G antenna system.

Interference scenario modeling can be broken down into three parts, as illustrated in Figure 4.

Figure 4 – The major components of RF interference modeling and simulation

In this case, we are concerned with a single 5G transmitter and a radar altimeter receiver. For purposes of this analysis, we won’t concern ourselves with interference in the other direction (from radar altimeter transmitter to the 5G receiver) but with Ansys EMIT it could be considered.

Emissions Model for the 5G C-Band Transmitter

The 5G Base Station model requires knowledge of its wideband electromagnetic emissions — both within its 5G channels and its out-of-band emissions. Any transmitter that carries messages in the RF signal has out-of-band emissions because of signal modulation, and the FCC and the International Telecommunication Union (ITU) set regulatory limits on the levels of signal transmitted by any licensed (or unlicensed) transmitter. The transmitter is fixed — sitting on the ground or on a fixed tower, but the antenna may have the ability to concentrate its energy in certain directions using a process called beam forming.

In the process of looking for interference potential, we study worst-case effects. In modeling the transmitter, we start with a peak power spectral mask, which shows the maximum power that is used at any frequency at any time. We can also capture effects like harmonics, intermodulation products, broadband noise, narrowband noise, and so forth, but one of the best ways to start is by using the industry regulatory requirements for maximum emissions. The International Telecommunications Union (ITU) sets these standards to ensure safety to people and systems due to RF level exposures. For our examination, we have started by using the specifications for a Wide Area Coverage C-Band base station with a 16-by-16 array, as set forward in the 3GPP Specifications. (If you’re interested in digging into the details, you can find it here.) I should mention that telecom equipment providers may (and do) provide equipment with broadband noise performance that exceeds the values we used; we start with the requirement as this represents a worst-case for a compliant transmitter. In fact, in a supporting study to the FAA by the Radio Technical Commission for Aeronautics (RTCA), we found a number of helpful parameters for defining the 5G radio emissions mask.

Figure 5 shows the 5G transmitter emission models used in our simulations, and we considered the currently available band at 3.7-3.8 GHz, in addition to the proposed future bands at 3.8-3.9 GHz and 3.9-3.98 GHz.

Figure 5 – The wideband emissions mask specification for the 5G C-band transmitters. Current implements involve only the 100 MHz band from 3.7-3.8 GHz, but future spectrum has been purchased by telecom providers for the 100 MHz band at 3.8-3.9 GHz and the 80 MHz band from 3.9-3.98 GHz.

Receiver Susceptibility Model

The radar altimeter receiver also has a wideband performance characteristic. While it is designed to operate in the 4.2-4.4 GHz band, it can suffer degraded performance if other radios put sufficiently strong emissions into this band. In addition, it is potentially susceptible to radiation outside this band of operation. Radio system designers often look at wideband receiver performance with a metric called susceptibility, which is generally a measure of how well a receiver can reject RF signals at any frequency. Within its band of operation, a receiver is intended to be very sensitive, therefore its susceptibility is very low. Outside its channel of operation, it is designed to be insensitive to incoming signals, so its susceptibility is very high at out-of-band frequencies.

A particular challenge in receiver design is balancing in-band or in-channel susceptibility with out-of-band susceptibility. A receiver might be very sensitive to signals within its band, but a consequence of this sensitivity may be that it can be overloaded by an out-of-band signal that is so strong that it defeats the receiver’s ability to reject it, resulting in a condition known as saturation.

Because saturation events can happen with strong transmission sources near our receiver, any good interference simulation needs to consider the receiver’s sensitivity and saturation characteristics for both the in-channel and the out-of-band signals.

While researching radar altimeter performance models, we found that there are wide performance variations. Arguably the best altimeter systems are used for commercial passenger aircraft, and indeed this is reflected in the types of aircraft that have now been approved for landing at the designated airports under low-visibility conditions. In our effort to develop a model for this demonstration, we looked for a “middle of the road” system to represent the radar altimeter susceptibility.

To formulate our model, we found a useful resource in the RTCA study, choosing an altimeter with good wideband characteristics (to yield the best altitude measurement resolution), along with a “reasonably good” receiver saturation level of -10 dBm. This means that the radar should have reasonable performance to reject signals outside of its intended frequency of operation. Figure 6 shows the receiver susceptibility model that we are using for this interference study, based on parameters listed in the RTCA study.

Figure 6 – Receiver susceptibility of a candidate radar altimeter operating at center frequency of 4.3 GHz. Most high-resolution aviation altimeters use 170 MHz of spectrum for measuring range from aircraft to ground.

With models for transmitter emissions and receiver susceptibility, we have two of the three important components of any interference analysis. The third component will be the wireless channel, depicted in Figure 4. We’ll cover the wireless channel and consider an interference analysis for a worst-case scenario in our next blog installment.

Also read:

5G and Aircraft Safety: Simulation is Key to Ensuring Passenger Safety

5G and Aircraft Safety: Simulation is Key to Ensuring Passenger Safety – Part 3

5G and Aircraft Safety: Simulation is Key to Ensuring Passenger Safety – Part 4