Lam Research pits process engineers against AI
Using artificial intelligence (AI) in process development for chip fabrication can cut costs by half, says a new study by Lam Research by putting process engineers head-to-head against AI-enabled computer algorithms.
The study looks at reducing development costs and accelerating the pace of innovation with a “human first, computer last” approach can reach process engineering targets dramatically faster and at half the cost compared to today’s approach.
“New approaches in innovation are needed to enable the industry to scale fast enough to meet the data-driven world’s evolving demand for next-generation chips,” said Tim Archer, president and chief executive officer at Lam Research, one of the leading semiconductor process equipment makers.
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In the Lam study, machine and human participants competed to create a targeted process development recipe at the lowest cost, weighing a variety of factors associated with test batches, metrology and overhead expenses. The study concluded that while humans excelled in solving challenging and out-of-the-box problems, a hybrid human first, computer last strategy can help address the tedious aspects of process development and, ultimately, speed up process engineering innovation.
“The opportunity for greater collaboration between talented engineers and machines in process engineering highlighted in Lam’s study in Nature is a potential game-changer for our customers and our industry at large. This research is a testament to Lam’s more than 40-year heritage of industry leadership and semiconductor manufacturing innovation,” said Archer.
Hundreds of steps are required for a chip process, typically with multiple instances of depositing thin layers of materials onto silicon wafers and etching away excess material with atomic-scale precision. This essential phase of semiconductor development is currently done by human engineers, largely using their intuition and a “trial and error” approach.
There are more than 100 trillion possible options to incorporate, making process development can be laborious, time-intensive, and costly. This can slow down the time needed to achieve the next technology breakthrough.
“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 percent 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.”
Lam is currently incorporating the key learnings from the study into its development operations. The Lam 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.
“By complementing engineering expertise with AI using the human first, computer last approach, the tedious and laborious aspects of design are alleviated for engineers, freeing them up to focus on the creative areas of development and explore innovations that may have been out of reach either due to bandwidth or cost,” said Keren Kanarik, technical managing director of Lam Research, lead author of the research paper and a former process engineer.
“While the application of AI in process engineering is still in its infancy and human expertise and domain knowledge is essential for the foreseeable future, the results point us to a path to foundationally change the way processes are developed for manufacturing chips.”
www.lamresearch.com; www.nature.com/articles/s41586-023-05773-7
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