Abstract
In a semiconductor fab, wafer lots are processed in complex sequences with re-entrants and parallel machines. It is necessary to ensure smooth wafer lot flows by detecting potential disturbances in a real-time fashion to satisfy the wafer lots’ demands. This study aims to identify production factors that significantly affect the system’s throughput level and find the best prediction model. The contributions of this study are as follows: (1) this is the first study that applies machine learning techniques to identify important real-time factors that influence throughput in a semiconductor fab; (2) this study develops a test bed in the Anylogic software environment, based on the Intel minifab layout; and (3) this study proposes a data collection scheme for the production control mechanism. As a result, four models (adaptive boosting, gradient boosting, random forest, decision tree) with the best accuracies are selected, and a scheme to reduce the input data types considered in the models is also proposed. After the reduction, the accuracy of each selected model was more than 97.82%. It was found that data related to the machines’ total idle times, processing steps, and machine E have notable influences on the throughput prediction.
Highlights
A semiconductor fab operates continuously to produce wafer lots through a complex process
Identifying important factors that affect the throughput prediction is important for production control, because it helps the shop floor managers to focus their observations on those important factors and ensure smooth wafer lot flows within the system
Data from 10,086 weeks were collected through the simulation after removing the records that were obtained during the warmup period
Summary
A semiconductor fab operates continuously to produce wafer lots through a complex process. The high complexity comes from the re-entrances of the wafer lots into the same machines several times [1]. For effective operating of this complex system, an advanced technique is necessary, which allows us to capture the system’s dynamics. The purpose of this study was to identify production factors that significantly affect the throughput level in the semiconductor fab and find the best prediction model. Machine learning techniques were applied to understand the relationships between real-time system status and planned throughput per week. Identifying important factors that affect the throughput prediction is important for production control, because it helps the shop floor managers to focus their observations on those important factors and ensure smooth wafer lot flows within the system
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