Nonlinear Variation Decomposition of Neural Networks for Holistic Semiconductor Process Monitoring

Nonlinear Variation Decomposition of Neural Networks for Holistic Semiconductor Process Monitoring

Artificial intelligence (AI) is increasingly used to solve multi‐objective problems and reduce the turnaround times of semiconductor processes. However, only brief AI explanations are available for process/device/circuit engineers to provide holistic feedback on the manufactured results. Herein, lin...

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Bibliographic Details
Main Authors: Hyeok Yun, Hyundong Jang, Seunghwan Lee, Junjong Lee, Kyeongrae Cho, Seungjoon Eom, Soomin Kim, Choong‐Ki Kim, Hong‐Chul Byun, Seongjoo Han, Min‐Soo Yoo, Rock‐Hyun Baek
Format: Article
Language:English
Published: Wiley 2024-10-01
Series:Advanced Intelligent Systems
Subjects:
dynamic random‐access memories
electronic devices
mathematics and computer sciences
neural networks
peripheral transistors
semiconductor manufacturings
Online Access:https://doi.org/10.1002/aisy.202300920

Internet

https://doi.org/10.1002/aisy.202300920

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