دورية أكاديمية
Human-machine collaboration for improving semiconductor process development.
| العنوان: | Human-machine collaboration for improving semiconductor process development. |
|---|---|
| المؤلفون: | Kanarik KJ; Lam Research Corporation, Fremont, CA, USA., Osowiecki WT; Lam Research Corporation, Fremont, CA, USA., Lu YJ; Lam Research Corporation, Fremont, CA, USA., Talukder D; Lam Research Corporation, Fremont, CA, USA., Roschewsky N; Lam Research Corporation, Fremont, CA, USA., Park SN; Lam Research Corporation, Fremont, CA, USA., Kamon M; Lam Research Corporation, Fremont, CA, USA., Fried DM; Lam Research Corporation, Fremont, CA, USA., Gottscho RA; Lam Research Corporation, Fremont, CA, USA. Richard.Gottscho@lamresearch.com. |
| المصدر: | Nature [Nature] 2023 Apr; Vol. 616 (7958), pp. 707-711. Date of Electronic Publication: 2023 Mar 08. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 0410462 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-4687 (Electronic) Linking ISSN: 00280836 NLM ISO Abbreviation: Nature Subsets: PubMed not MEDLINE; MEDLINE |
| أسماء مطبوعة: | Publication: Basingstoke : Nature Publishing Group Original Publication: London, Macmillan Journals ltd. |
| مستخلص: | One of the bottlenecks to building semiconductor chips is the increasing cost required to develop chemical plasma processes that form the transistors and memory storage cells 1,2 . These processes are still developed manually using highly trained engineers searching for a combination of tool parameters that produces an acceptable result on the silicon wafer 3 . The challenge for computer algorithms is the availability of limited experimental data owing to the high cost of acquisition, making it difficult to form a predictive model with accuracy to the atomic scale. Here we study Bayesian optimization algorithms to investigate how artificial intelligence (AI) might decrease the cost of developing complex semiconductor chip processes. In particular, we create a controlled virtual process game to systematically benchmark the performance of humans and computers for the design of a semiconductor fabrication process. We find that human engineers excel in the early stages of development, whereas the algorithms are far more cost-efficient near the tight tolerances of the target. Furthermore, we show that a strategy using both human designers with high expertise and algorithms in a human first-computer last strategy can reduce the cost-to-target by half compared with only human designers. Finally, we highlight cultural challenges in partnering humans with computers that need to be addressed when introducing artificial intelligence in developing semiconductor processes. (© 2023. The Author(s).) |
| التعليقات: | Comment in: Nature. 2023 Apr;616(7958):667-668. doi: 10.1038/d41586-023-01353-x. (PMID: 37085613) |
| References: | J Exp Psychol Gen. 2015 Feb;144(1):114-26. (PMID: 25401381) Nature. 2017 Oct 18;550(7676):354-359. (PMID: 29052630) Nature. 2021 May;593(7857):33-36. (PMID: 33947992) |
| تواريخ الأحداث: | Date Created: 20230308 Date Completed: 20230505 Latest Revision: 20240727 |
| رمز التحديث: | 20240727 |
| مُعرف محوري في PubMed: | PMC10132970 |
| DOI: | 10.1038/s41586-023-05773-7 |
| PMID: | 36890235 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 1476-4687 |
|---|---|
| DOI: | 10.1038/s41586-023-05773-7 |