PDF Solutions Calls for a Revolution in Semiconductor Collaboration at SEMICON West

SEMICON West was held in Phoenix, Arizona on October 7-9. This premier event brings the incredibly diverse global electronics supply chain together to address the semiconductor ecosystem’s greatest opportunities and challenges. The event’s tagline this year is:

Stronger Together — Shaping a Sustainable Future in Talent, Technology, and Trade, underscoring the semiconductor industry’s commitment to collaboration in addressing key challenges and opportunities. One of the participants at this event presented an approach and technology that form the foundation for global collaboration across the semiconductor industry. Let’s look at how PDF Solutions used the SEMICON West stage to call for a revolution in semiconductor collaboration.

PDF Solutions at the Show

PDF highlighted its game-changing technology during SEMICON West with product demos, daily booth presentations and speaking engagements in the SEMICON West program. Attendees at the PDF booth interacted with a virtual reality model of semiconductor manufacturing chamber equipment and the associated connection to PDF’s EquipmentTwin solution. Other demos illustrated the latest products that are shaping the future of the semiconductor industry.

A Keynote Aimed at Catalyzing Collaboration

John Kibarian, CEO of PDF Solutions, presented an important keynote during SEMICON West. His talk was titled Revolutionizing Semiconductor Collaboration: The Emergence of AI-Driven Industry Platforms.  

John pointed out that the semiconductor industry thrives on innovation, and this work is fueled by collaboration. But the nature of this collaboration is changing. The growing technology trend towards 3D and hybrid packaging is creating a larger and more complex global supply chain. He explained that the ability to leverage AI at all levels to drive operational efficiency is needed.

John used a couple of visuals to make his point. First, he illustrated the nature of collaboration driven by the current model, which is largely reactionary and uses simple linear handoffs by sharing relatively small amounts of data as shown below.

He went on to point out the substantial changes that technologies such as 3D are bringing to the collaboration model. Some of those drivers are summarized in the graphic below.

He explained that what is needed is a new, AI-driven collaboration model that is depicted in the graphic at the top of this post. He detailed the collaboration that will be required both within the enterprise and across the global supply chain. The many aspects of the required collaboration across the supply chain are shown in the graphic below.

John then distilled the key building blocks or foundational capabilities required to build the platform needed to enable AI-driven collaboration across the industry:

• A secure data infrastructure
• Automated orchestration
• AI agents

He explained that PDF Solutions has already deployed a secure data infrastructure to enable global collaboration. It is called secureWISE™ and it connects over 300 manufacturing locations and manages exabytes of data between fabs and OEMs. Over 100 OEMs are connected, and the system is ISO: 27001 compliant. He reported there have been zero security breaches in over 20 years. An impressive statistic.

John then went on to describe how secureWISE provides secure remote connectivity for the semiconductor industry. Technologies used include end-to-end secure access across private networks, virus scan for secure equipment software updates, and granular user permission for remote control and optimization. With this secure remote connection data infrastructure, equipment OEMs are able to remotely execute software upgrades and use collected data to deliver AI based value added equipment optimization services.  For Fab operators connecting their equipment with secureWISE increases fab uptime and improve operational efficiency.

The secure global data infrastructure is the foundation on top of which global collaboration can be executed relying on automated orchestrations which enable to Align and Abstract data across the supply chain to accelerate decisions. This is achieved by:

• Using actionable detailed manufacturing data to drive business decisions
• Automating business processes within and across enterprises
• Deploying and orchestrating AI agents to automate and accelerate decisions

With this approach business decisions can be made much faster, using more timely and accurate data coming from across multiple applications and organizations.

Examples include:

• Product Costing: Accurate and up-to-date costing information based on actual resource consumption
• Order Status: Real-time updates on orders status and yield
• Quality: Rapid identification and isolation of at-risk materials
• Test Flow: Automated test flow management
• WIP: Real-time WIP tracking and management across the supply chain

In most manufacturing organizations a very small amount of the collected manufacturing data is actually used for Analysis. PDF Solutions estimates that at most 5% of manufacturing data is used for meaningful analytics. To fully leverage and scale the use of AI semiconductor companies need to be able automate a lot of the analytics processes while enabling human governance for this AI execution. Aspects of this work include:

• Enforce Data Quality: Humans define standards; AI implements data quality
• Humans Set Collaboration Rules: To define collaboration principles, data sharing, and security boundaries
• AI Executes at Scale: Operating autonomously within those boundaries, handling complex, high-volume tasks
• Cross-Organization Governance: Allows fabs to define vendor access and data protocols and AI manages daily execution

An example of this that John provided is guided analytics, as a process that mines 100% of the data, 100% of the time, and automates up to 90% of the analysis. He went on to describe how seamless integration of data types is achieved. For example: hard bin, soft bin, parametric, PCM, test tools, and units per hour (UPH). This process delivers an issue-based flow that uses AI/ML powered diagnosis, resulting in the ability to render data visualization in seconds.

John went on to describe many valuable and high-impact capabilities that are enabled by AI agents including:

• Predictive test to predict future test results so test dollars can be spent on units that need the most attention
• Predictive burn-in to predict which burn-in will pass to eliminate costly and unnecessary burn-in runs
• Predictive binning to predict which tests will fail – finding failures early saves time and money

John concluded the story with an overall collaboration vision and a call to action for the industry to step up its work in this area. The vision John presented, which provides a pathway to greater worldwide semiconductor innovation, is summarized below.

To Learn More

The work presented at SEMICON West illustrates the significant progress PDF Solutions is making to revolutionize semiconductor collaboration. John’s keynote underscored the importance of worldwide adoption of these new strategies. You can access John Kibarian’s keynote on the PDF Solutions website. You can learn more about what PDF is doing on SemiWiki here.  You can also explore the suite of integrated products offered by the company here.  And that’s how PDF Solutions calls for a revolution in semiconductor collaboration at SEMICON West.