Machine Learning Powered Capacity Planning for Semiconductor Fab

Semiconductor wafers are manufactured by stacking hundreds of layers engraved with circuit patterns. Wafer fabrication process with the characteristic of re-entrant flow is a complex job-shop that consists of several work areas such as lithography, etch, and diffusion. Each work area has several wor...

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Bibliographic Details
Main Authors: Ko, Keyhoon, Chang, Seokcheol, Lee, Won-Jun, Kim, Byung-Hee
Format: Conference Proceeding
Language:English
Subjects:
Fabrication
Lithography
Machine learning
Optimization
Semiconductor device modeling
Stacking
Workstations
Online Access:Request full text
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Description
Summary:Semiconductor wafers are manufactured by stacking hundreds of layers engraved with circuit patterns. Wafer fabrication process with the characteristic of re-entrant flow is a complex job-shop that consists of several work areas such as lithography, etch, and diffusion. Each work area has several workstations with one or more machines that execute the same operation. Capacity planning for a wafer fab is difficult; one must determine the required machine count to meet demands on time. This study proposes a methodology to find the optimal machine count for each workstation using an approach that combines optimization, simulation, and machine learning techniques. The experimental example demonstrates that this approach can systematically provide a good and practical solution.
ISSN:1558-4305
DOI:10.1109/WSC57314.2022.10015311