Impact of integrating equipment health in production scheduling for semiconductor fabrication

Kao, Yu-Ting; Dauzère‐Pérès, Stéphane; Blue, Jakey; Chang, Soon-Jyh

doi:10.1016/j.cie.2018.04.053

Cited by 25 publications

(10 citation statements)

References 33 publications

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“…They developed a CP model, and two improvement procedures for the SCH and QCH, which generates better solutions than [95]. Kao et al considered the machine health factor (MHF), which indicates that job quality risk is modeled by the MHF function and machine processing capability [97]. They proposed two MILP formulations in the case of static and dynamic MHF separately.…”

Section: Non-batch Machine Scheduling Problemsmentioning

confidence: 99%

Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey

Fang,

Cheang,

Lim

2023

Sustainability

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Machine scheduling problems associated with semiconductor manufacturing operations (SMOs) are one of the major research topics in the scheduling literature. Lots of papers have dealt with different variants of SMOs’ scheduling problems, which are generally difficult to tackle theoretically and computationally. In this paper, the single machine, parallel machines, flow shops, and job shops scheduling problems from SMOs have been reviewed, based on different processing constraints, e.g., batch processing, auxiliary resources, incompatible job families, and reentrant flow, etc., with the cycle time, flow time, and throughput-related performance measures. Given the vast and diverse nature of the current literature, it is urgently needed to make a systematic survey in order to identify the important research problems, research trends, and the progress of the related solution methods, as well as clarify future research perspectives. We hope the findings and observations could provide some insights to the researchers and practitioners in this domain.

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Section: Non-batch Machine Scheduling Problemsmentioning

confidence: 99%

Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey

Fang,

Cheang,

Lim

2023

Sustainability

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“…Semiconductor processes increase productivity through facility diagnosis, process control, stabilization of yield rate, and so on. In addition, the semiconductor fabrication process has been continually refined, and design complexity has increased to enhance productivity and semiconductor accumulation [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Pre-Training of Imbalanced Data for Identification of Wafer Map Defect Patterns

et al. 2021

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Visual defect inspection and classification are significant steps of most manufacturing processes in the semiconductor and electronics industries. Known and unknown defects on wafer maps tend to cluster, and these spatial patterns provide valuable process information for supporting manufacturing in determining the root causes of abnormal processes. In previous studies, data augmentation-based deep learning (DL) techniques were most commonly used for the identification of wafer map defect patterns (WMDP). Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations were manually designed for the WMDP problem. In this study, we propose a DL-based method with automatic data augmentation for the WMDP task. Basically, it focuses on learning effective discriminative features, from wafer maps, through a deep network structure. The network consists of a convolution-based variational autoencoder (CVAE) sequentially. First, we pre-trained the CVAE on large training data in an unsupervised manner. Second, we fine-tuned the encoder of the CVAE, which was followed by a neural network (NN) classifier, in a supervised manner. Additionally, we describe a simple procedure for automatically searching for improved data augmentation policies. The policy mainly consists of five image processing functions: rotation, flipping, shifting, shearing range, and zooming. The effectiveness of the proposed method was demonstrated through experimental results obtained from a simulation dataset and a real-world wafer map dataset (WM-811K). This study provides guidance for the application of deep learning in semiconductor manufacturing processes to improve product quality and yield.INDEX TERMS Classification, convolutional variational autoencoder, deep learning, imbalanced data, neural network, unsupervised pre-training, variational autoencoder, wafer map defect patterns.

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“…In the performance optimization process, the possible correlations among performance indicators should be first analyzed. All performance attributes can be balanced using this method, and thus, an optimal algorithm for performance optimization can be designed [16] . Many common methods have been developed for correlation analyses, such as the Pearson correlation coefficient method and canonical correlation analysis (CCA) [17] .…”

Section: Introductionmentioning

confidence: 99%

Evaluation System and Correlation Analysis for Determining the Performance of a Semiconductor Manufacturing System

Zhao

et al. 2021

Complex Syst. Model. Simul.

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Numerous performance indicators exist for semiconductor manufacturing systems. Several studies have been conducted regarding the performance optimization of semiconductor manufacturing systems. However, because of the complex manufacturing processes, potential complementary or inhibitory correlations may exist among performance indicators, which are difficult to demonstrate specifically. To analyze the correlation between the performance indicators, this study proposes a performance evaluation system based on the mathematical significance of each performance indicator to design statistical schemes. Several samples can be obtained by conducting simulation experiments through the performance evaluation system. The Pearson correlation coefficient method and canonical correlation analysis are used on the received samples to analyze linear correlations between the performance indicators. Through the investigation, we found that linear and other complex correlations exist between the performance indicators. This finding can contribute to our future studies regarding performance optimization for the scheduling problems of semiconductor manufacturing.

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Impact of integrating equipment health in production scheduling for semiconductor fabrication

Cited by 25 publications

References 33 publications

Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey

Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey

Unsupervised Pre-Training of Imbalanced Data for Identification of Wafer Map Defect Patterns

Evaluation System and Correlation Analysis for Determining the Performance of a Semiconductor Manufacturing System

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