{"id":8123,"date":"2026-02-09T13:11:06","date_gmt":"2026-02-09T04:11:06","guid":{"rendered":"https:\/\/aizoth.com\/?post_type=use_case&p=8123"},"modified":"2026-02-09T13:11:06","modified_gmt":"2026-02-09T04:11:06","slug":"contents-e027","status":"publish","type":"use_case","link":"https:\/\/aizoth.com\/en\/use_case\/contents-e027\/","title":{"rendered":"Multi-Process Optimization for Semiconductor Fabrication Using Multi-Sigma\u00ae"},"content":{"rendered":"\n

Multi-Sigma\u00ae<\/sup>\u2019s AI Chain Analysis integrates multiple AI models into a unified pipeline to achieve multi-process optimization.<\/strong><\/strong><\/strong><\/p>\n\n\n\n

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1. AI Chain Analysis with Multi-Sigma\u00ae<\/sup><\/strong><\/strong><\/h2>\n\n\n\n

Multi-Sigma\u00ae<\/sup> can build a predictive model for lithography\/etching\/cleaning process based on process conditions and the corresponding measured performance metrics. Then, Multi-Sigma\u00ae<\/sup> AI Chain Analysis integrates multiple AI models into a unified pipeline to achieve multi-process optimization.<\/p>\n\n\n\n

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\"AI<\/figure>\n<\/div>\n\n\n
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2. Contribution Analysis across Multiple Processes<\/strong><\/strong><\/h2>\n\n\n\n

Multi-Sigma\u00ae\u2019s contribution analysis identifies process conditions that positively or negatively impact the performance metrics. For example, the figure on the right shows the top five process parameters that are most effective in reducing defect density across multiple processes.<\/p>\n\n\n\n

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\"Contribution<\/figure>\n\n\n\n
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3. Multi-Process Optimization with Multi-Sigma\u00ae<\/sup><\/strong><\/h2>\n\n\n\n

Multi-Sigma\u00ae<\/sup>\u2019s optimization suggests the optimal combination of process conditions across multiple processes under specified constraints.<\/p>\n\n\n\n

Expected outcomes:<\/strong><\/p>\n\n\n\n