Accurate Prediction and Reliable Parameter Optimization of Neural Network for Semiconductor Process Monitoring and Technology Development

Accurate Prediction and Reliable Parameter Optimization of Neural Network for Semiconductor Process Monitoring and Technology Development

Herein, novel neural network (NN) methods that improve prediction accuracy and reduce output variance of the optimized input in the gradient method for cross‐sectional data are proposed, and the variability evaluation approach of optimized inputs in the semiconductor process is suggested. Specifical...

Full beskrivning

Bibliografiska uppgifter
Huvudupphovsmän: Hyeok Yun, Chang-Hyeon An, Hyundong Jang, Kyeongrae Cho, Jeong-Sik Lee, Seungjoon Eom, Choong-Ki Kim, Min-Soo Yoo, Hyun-Chul Choi, Rock-Hyun Baek
Materialtyp: Artikel
Språk:English
Publicerad: Wiley 2023-09-01
Serie:Advanced Intelligent Systems
Ämnen:
gradient descent method
holistic parameter optimization
machine learning
network ensemble
neural networks
semiconductor manufacturing
Länkar:https://doi.org/10.1002/aisy.202300089

Internet

https://doi.org/10.1002/aisy.202300089

Liknande verk