How Digital Twins Can Bridge the U.S. Chipmaking Gap

Can we alleviate our overreliance 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 distribute $50 billion of the CHIPS Act in subsidies to build chip factories in the United States and support chip research and development in the United States. . Just this month, new restrictions were placed on China’s ability to buy and manufacture some high-end chips used in military applications. Export controls also impact US companies exporting any semiconductor manufacturing equipment to China.

Already, companies are 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, expected to be operational by 2025 and one of the largest silicon manufacturing sites in the world. Taiwan Semiconductor (TSMC) and Samsung (which has already announced plans to open a $17 billion chip factory in Texas in 2024) have also pledged to bring chip manufacturing back to US shores.

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

Digital twins – virtual representations that serve as real-time digital counterparts of physical objects or processes – have evolved significantly since their first practical application at NASA in 2010 to improve simulation of the physical model of spacecraft.

Today’s digital twin technology allows chip makers to improve performance while operating at full capacity without any downtime. Companies such as LAM Research, Bosch (which uses a digital twin in one of its German semiconductor factories) and Applied Materials (a leader in materials engineering solutions used to produce virtually all new chips and displays advanced in the world) are already using substitutes for machine learning models that are more accurate and up to a million times faster than traditional physics-based simulations.

Tech startups such as Tignis (one of our portfolio companies), AspenTech and Ansys are now at the forefront of advancements, leveraging digital twins to optimize industrial operations and make AI and ML available for almost any application.

With AI poised to play a key role in process control and modeling and available for use in any area of ​​engineering simulation, there will be a huge opportunity for disruption in the manufacturing industry through delivering significant improvements in performance, quality, and throughput.

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

Yet, while some chipmakers are already using digital twins to create development models, the technology has not been widely used to optimize production. This is surprising considering that by using data already available, digital twins have the ability to help chipmakers better determine whether production goals are adequate and, if not case, what even a modest increase in production might mean.

By recreating what a physical system looks like in the cloud, manufacturers can identify key learnings and achieve an even greater increase in capacity, all without the risks associated with traditional methods.

With private and public funds at stake, digital twins could be a game-changer for chipmakers, manufacturers and consumers.

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

The opinions expressed in comments are solely the opinions of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

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