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However, only brief AI explanations are available for process\/device\/circuit engineers to provide holistic feedback on the manufactured results. Herein, linear\/nonlinear variation decomposition (LVD\/NLVD) of neural networks is demonstrated to quantitatively evaluate the influence of unit processes on the figure of merit (FoM) and co\u2010analyze the unit process influences with device characteristic behaviors. The NLVD can evaluate the output variation from each input of neural networks in an individual sample, although neural networks are not available in an analytic form. The NLVD is successfully verified by confirming that a) the output and summation of all decomposed output variations perfectly coincide and b) the process influences on the FoM are decomposed to 6.01\u201354.86% more accurately compared with those of LVD in 1Y nm node dynamic random\u2010access memory test vehicles with a baseline and split tests introducing high\u2010k metal gates with a minimum gate length of 1\u2009A\u2009nm node for further node scaling. The approaches identify defective processes and defect mechanisms in each sample and wafer, which enhance causal analyses for individual cases in diverse fields based on regression artificial neural networks.<\/jats:p>","DOI":"10.1002\/aisy.202300920","type":"journal-article","created":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T07:34:33Z","timestamp":1725435273000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Nonlinear Variation Decomposition of Neural Networks for Holistic Semiconductor Process Monitoring"],"prefix":"10.1002","volume":"6","author":[{"given":"Hyeok","family":"Yun","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering Pohang University of Science and Technology  Pohang 37673 Republic of Korea"}]},{"given":"Hyundong","family":"Jang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Pohang University of Science and Technology  Pohang 37673 Republic of Korea"}]},{"given":"Seunghwan","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Pohang University of Science and Technology  Pohang 37673 Republic of Korea"}]},{"given":"Junjong","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Pohang University of Science and Technology  Pohang 37673 Republic of Korea"}]},{"given":"Kyeongrae","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Pohang University of Science and Technology  Pohang 37673 Republic of Korea"}]},{"given":"Seungjoon","family":"Eom","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Pohang University of Science and Technology  Pohang 37673 Republic of Korea"}]},{"given":"Soomin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Pohang University of Science and Technology  Pohang 37673 Republic of Korea"}]},{"given":"Choong\u2010Ki","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of DRAM Development SK Hynix Inc.  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