Interactive Q-Learning Approach for Pick-and-Place Optimization of the Die Attach Process in the Semiconductor Industry

Mathematical Problems in Engineering. 2019;2019 DOI 10.1155/2019/4602052

 

Journal Homepage

Journal Title: Mathematical Problems in Engineering

ISSN: 1024-123X (Print); 1563-5147 (Online)

Publisher: Hindawi Limited

LCC Subject Category: Technology: Engineering (General). Civil engineering (General) | Science: Mathematics

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

 

AUTHORS

Gilseung Ahn (Department of Industrial and Management Engineering, Hanyang University, Ansan 15588, Republic of Korea)
Myunghwan Park (Department of Industrial and Management Engineering, Hanyang University, Ansan 15588, Republic of Korea)
You-Jin Park (Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan)
Sun Hur (Department of Industrial and Management Engineering, Hanyang University, Ansan 15588, Republic of Korea)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 26 weeks

 

Abstract | Full Text

In semiconductor back-end production, the die attach process is one of the most critical steps affecting overall productivity. Optimization of this process can be modeled as a pick-and-place problem known to be NP-hard. Typical approaches are rule-based and metaheuristic methods. The two have high or low generalization ability, low or high performance, and short or long search time, respectively. The motivation of this paper is to develop a novel method involving only the strengths of these methods, i.e., high generalization ability and performance and short search time. We develop an interactive Q-learning in which two agents, a pick agent and a place agent, are trained and find a pick-and-place (PAP) path interactively. From experiments, we verified that the proposed approach finds a shorter path than the genetic algorithm given in previous research.