Lam Research has published a new study that uses artificial intelligence (AI) in a hybrid chip fabrication process that reduces development costs and accelerates time-to-market.
Under the study, the human first, computer last approach can reach process engineering targets dramatically faster and at half the cost compared to current technology. The goal is to ease semiconductor production as the market moves toward $1 trillion in annual revenue by 2030.
With the complexity of chips rising, process development is becoming more challenging and expensive. Researchers at Lam put engineers against AI-enabled computer algorithms in the study.
When chips are designed, hundreds of steps are required for these nanometer-sized devices on a silicon wafer. These steps include multiple instances of depositing thin layers of materials onto silicon wafers and etching away excess material with atomic-scale precision. As these chips become more complex, 100 trillion possible options are available. Because of this process, development can be laborious, time-intensive and costly.
About the study
Lam had the machine and human participants compete to create targeted process development at the lowest cost, weighing a variety of factors associated with test batches, metrology and overhead expenses.
The study concluded that while humans can solve challenges and out-of-the-box problems, a hybrid human first, computer last strategy can help address the tedious aspects of process development and speed up processing engineering innovation.
Lam said it plans to incorporate the key learnings from the study into its development operations. The study provides initial guidance on how to successfully integrate human knowledge, skill and experience with AI’s ability to rapidly assess numerous possible combinations in process engineering, Lam said.
“Although critical to the creation of each and every chip produced, the plasma physics of process engineering has been for decades rooted in the same scientific approach that Thomas Edison used: trial and error,” said Rick Gottscho, executive vice president and strategic advisor to the CEO – Innovation Ecosystem at Lam Research and co-author of the study. “Our research showed that while engineering talent remains essential to innovation, process engineering costs can be reduced by 50% by integrating AI at the right stage and with the right data. The study provides a prescriptive approach for bringing together the best of human-led engineering and the best of what data science and machines offer to create a combination that performs better than either one alone. If realized, this hybrid approach can lead to significant savings in both dollars and engineering time for the industry.”
The full research can be found in the journal Nature.
