Wafer Plot Classification Using Neural Networks and Tensor Methods

A. Wahba, Chuanhe Jay Shan, Li-C. Wang, N. Sumikawa
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引用次数: 4

Abstract

This paper presents an automated flow to classify wafer plots obtained based on production test data. The wafer plots are based on pass/fail locations. The classification is achieved through wafer pattern recognition models built with two sets of techniques, Generative Adversarial Networks and Tensor analysis. The primary focus is on developing the automatic flow. Experiment results based on production test data from a microcontroller product line will be presented to demonstrate the usefulness of the proposed classification flow.
基于神经网络和张量方法的晶圆图分类
本文提出了一种基于生产试验数据的晶圆图自动分类流程。晶圆图基于合格/不合格位置。该分类是通过使用生成对抗网络和张量分析两套技术建立的晶圆模式识别模型来实现的。主要的重点是发展自动流程。基于微控制器产品线的生产测试数据的实验结果将展示所提出的分类流程的有效性。
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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