Deep reinforcement learning framework for end-to-end semiconductor process control

Thomas Hirtz; He Tian; Shazrah Shahzad; Fan Wu; Yi Yang; Tian‐Ling Ren

doi:10.1007/s00521-024-09710-1

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Deep reinforcement learning framework for end-to-end semiconductor process control

Thomas Hirtz He Tian Shazrah Shahzad Fan Wu Yi Yang Tian‐Ling Ren

Year: 2024 Journal: Neural Computing and Applications Vol: 36 (20)Pages: 12443-12460 Publisher: Springer Science+Business Media

DOI: 10.1007/s00521-024-09710-1

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Keywords:

End-to-end principle Process (computing) Reinforcement learning Computer science Control (management) Reinforcement Artificial intelligence Psychology Social psychology Programming language

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Industrial Vision Systems and Defect Detection

Physical Sciences → Engineering → Industrial and Manufacturing Engineering

Silicon and Solar Cell Technologies

Physical Sciences → Engineering → Electrical and Electronic Engineering

Advancements in Semiconductor Devices and Circuit Design

Physical Sciences → Engineering → Electrical and Electronic Engineering

Deep reinforcement learning framework for end-to-end semiconductor process control

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