Leveraging Machine Learning for Capacity and Cost on a Complex Toolset: A Case Study

IF 2.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-09-12 DOI:10.1109/TSM.2023.3314431

Adar A. Kalir;Sin Kit Lo;Gavan Goldberg;Irena Zingerman-Koladko;Aviv Ohana;Yossi Revah;Tsvi Ben Chimol;Gavriel Honig

引用次数: 0

Abstract

In this case study, we introduce two ML techniques, Long Short-Term Memory (LSTM) and an optimized Random Forest (RF), to address challenges related to capacity and cost, by addressing problems of unscheduled downtime and Process Time (PT) variation in the case of a complex chamber processing tool. We show that by using these ML techniques, traditional methods of Predictive Maintenance (PdM) and PT analysis can be enhanced with new insights and lead to significant productivity improvements. We demonstrate that, with these methods, by detecting states and attributes of the tool, trends in the tool’s behavior can be more effectively identified to reduce its unscheduled downtime and improve its run-rate, thereby resulting in significant capacity and cost improvements. This is achieved by reducing the variability of availability; extending the Mean Time Between Failures (MTBF); and removing variability in PT between lots and chambers.

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利用机器学习在复杂工具集上获得容量和成本：一个案例研究

在本案例研究中，我们引入了两种ML技术，即长短期存储器（LSTM）和优化随机森林（RF），通过解决复杂腔室处理工具中的计划外停机时间和处理时间（PT）变化问题，来解决与容量和成本相关的挑战。我们表明，通过使用这些ML技术，传统的预测性维护（PdM）和PT分析方法可以得到新的见解，并显著提高生产力。我们证明，使用这些方法，通过检测工具的状态和属性，可以更有效地识别工具行为的趋势，以减少其计划外停机时间并提高其运行速度，从而显著提高容量和成本。这是通过减少可用性的可变性来实现的；延长平均无故障时间（MTBF）；以及消除批次和腔室之间PT的可变性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Semiconductor Manufacturing 工程技术-工程：电子与电气

CiteScore

5.20

自引率

11.10%

发文量

101

审稿时长

3.3 months

期刊介绍： The IEEE Transactions on Semiconductor Manufacturing addresses the challenging problems of manufacturing complex microelectronic components, especially very large scale integrated circuits (VLSI). Manufacturing these products requires precision micropatterning, precise control of materials properties, ultraclean work environments, and complex interactions of chemical, physical, electrical and mechanical processes.