How digital twin technology can bridge America’s chip manufacturing gap

Can we lessen our over-reliance on Asia for the microprocessors used in everything from the appliances in our homes to the laptops we use and the cars we drive?

In early September, the Department of Commerce unveiled its implementation plan to allocate $50 billion from the CHIPS Act for subsidies to build US chip factories and support US chip research and development. Just this month, new restrictions were imposed on China’s ability to buy and manufacture certain high-end chips for military applications. The export controls also affect US companies that export semiconductor manufacturing equipment to China.

Companies are already announcing investments to reduce US dependence on Asia for semiconductors. Intel Corp. plans to spend $20 billion on a new manufacturing facility in New Albany, Ohio, which is expected to be operational by 2025 and will be one of the world’s largest silicon manufacturing facilities. Taiwan Semiconductor (TSMC) and Samsung (which have already announced plans for a $17 billion chip fab in Texas to open in 2024) have also committed to bringing chip manufacturing back to US shores.

At the same time, investments in artificial intelligence (AI) and machine learning (ML) are increasing in the semiconductor industry to increase efficiency in ways we could never have imagined. In today’s era of extreme automation, AI along with digital twin technology has the ability to speed up the chip design and manufacturing process, and in turn, help us bridge the gap between supply and demand faster.

Digital twins — virtual representations that serve as real-time digital counterparts of physical objects or processes — have come a long way since their first practical application at NASA in 2010 to improve the simulation of physical models of spacecraft.

Today’s digital twin technology allows chipmakers to improve performance while operating at full capacity without interruption. Companies like LAM Research, Bosch (which uses a digital twin at one of its German semiconductor fabs) and Applied Materials (a leading provider of materials engineering solutions that are used to make virtually every new chip and advanced display in the world) are already using surrogate machine learning models , which are more accurate and up to a million times faster than traditional physics-based simulations.

Technology startups like Tignis (one of our portfolio companies), AspenTech and Ansys are now game-changers, using digital twins to streamline industrial operations and make AI and ML available for almost any application.

As AI is poised to play a key role in process control and process modeling, and available for use in all areas of engineering simulation, there will be a tremendous opportunity to disrupt the manufacturing industry by delivering significant improvements in yield, quality, and throughput .

Digital twin modeling can therefore prove invaluable to the chip manufacturing process, contributing to a streamlined design and production process while reducing reliance on physical prototyping.

While some chipmakers are already using digital twins to create development models, the technology has not yet been widely used to optimize production. This is surprising given that by using data already available, digital twins are able to help chipmakers better determine whether production targets are reasonable and, if not, what even a modest increase in production could mean.

By replicating what a physical system looks like in the cloud, manufacturers can gain important insights and achieve even greater increases in capacity—all without the risks associated with traditional methods.

With private and public funds at stake, digital twins could be critical for chipmakers, manufacturers and consumers.

Chris Rust is the founder and general partner of Clear Ventures.

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