Routing (electronic design automation)

Macroeconomics

Engineering

Scheduling (production processes)

Operating system

Economics

Production line

Computer science

Mathematical optimization

Manufacturing engineering

Distributed computing

Computer network

Semiconductor industry

Mechanical engineering

Reinforcement learning

Mathematics

Wafer

Artificial intelligence

Deep learning

Artificial neural network

Semiconductor device fabrication

Machine learning

Production (economics)

Electrical engineering

Heuristics

Job shop scheduling

Despite producing tremendous success stories by identifying cat videos [1] or solving computer as well as board games [2], [3], the adoption of deep learning in the semiconductor industry is moderatre. In this paper, we apply Google DeepMind's Deep Q Network (DQN) agent algorithm for Reinforcement Learning (RL) to semiconductor production scheduling. In an RL environment several cooperative DQN agents, which utilize deep neural networks, are trained with flexible user-defined objectives. We show benchmarks comparing standard dispatching heuristics with the DQN agents in an abstract frontend-of-line semiconductor production facility. Results are promising and show that DQN agents optimize production autonomously for different targets.

Deep reinforcement learning for semiconductor production scheduling