Dynamic OHT Routing Using Travel Time Approximation Based on Deep Neural Network

Abstract

This study proposes an effective dynamic OHT routing approach to handle a substantial volume of wafer transport in a modern large semiconductor fabrication plant. The proposed approach aims to overcome challenges faced by previous approaches whose applicability is limited in conditions where the underlying distribution of traffic conditions varies over a short period. The approach comprises two models to explicitly approximate the congestion-aware travel times of different parts of the candidate route based on the current rail conditions, to evaluate the traffic conditions when routing. First, the local path approximation model heuristically evaluates the travel time of paths within a short range. Second, the global path approximation model evaluates the travel time of a distant range using a deep neural network. The simulation experiments show that the proposed approach outperforms the benchmark algorithms regarding delivery time and throughput, exhibiting 11.34% lower delivery time compared to a reinforcement-learning-based benchmark model. The proposed approach successfully integrates environmental information to evaluate congestion in a complex fab and optimize the routes of a large fleet of OHTs while balancing the traffic throughout a dynamic system.

Keywords

• Congestion-aware
• deep neural network
• dynamic vehicle routing
• large scale rail network
• overhead hoist transport
• semiconductor manufacturing