A yield diagnosis method basedon random forest for addressable WAT

LIU Yuan-xiu; SHI Zheng; ZHANG Pei-yong

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A yield diagnosis method basedon random forest for addressable WAT

更新时间：2026-09-04

- A yield diagnosis method basedon random forest for addressable WAT
- Vol. 36, Issue 9, Pages: 94-98(2019)
- 作者机构：
  
  浙江大学超大规模集成电路设计研究所,浙江,杭州,310027
- 作者简介：
- 基金信息：
- DOI：
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- Published：2019
- 稿件说明：
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刘元修, 史峥, 张培勇. 一种基于随机森林的可寻址WAT良率诊断方法[J]. 微电子学与计算机, 2019, 36(9): 94-98.

LIU Yuan-xiu, SHI Zheng, ZHANG Pei-yong. A yield diagnosis method basedon random forest for addressable WAT[J]. 2019, 36(9): 94-98.
刘元修, 史峥, 张培勇. 一种基于随机森林的可寻址WAT良率诊断方法[J]. 微电子学与计算机, 2019, 36(9): 94-98. DOI：

LIU Yuan-xiu, SHI Zheng, ZHANG Pei-yong. A yield diagnosis method basedon random forest for addressable WAT[J]. 2019, 36(9): 94-98. DOI：

摘要

WAT (Wafer Acceptance Test

晶圆允收测试)是晶圆在完成制造后必须通过的一项电学测试.本文提出了一种基于随机森林的良率诊断方法

使用随机森林算法对来自可寻址WAT的测试数据建立分类模型

并从中提取关键规则.提取出的规则集合可以帮助分析人员快速、准确地对生产中导致低良率的根本原因进行定位、分析

对于良率提升有着重要意义.在真实可寻址WAT测试数据集上应用本文提出的方法

得到的规则集合在分类性能上较基于决策树的方法有着更好的表现.

Abstract

WAT(Wafer Acceptance Test) is an electrical test that a wafer must pass upon completion of fabrication. An add ressable WATyield diagnosis method is proposed in this paper

which uses the random forest algorithm to establish a classification model on test data and extractskey rules.The extracted rules can help analysts quickly and accurately locate the rootcause of low yield in production

which plays a key role in yield enhancement. The proposed method hasbeen experimented on dataset from arealaddressable WAT

and the obtained rule set has better classification performance than the rule set obtained by method based on decision tree.

关键词

Keywords

references

LEE F. Advanced yield enhancement: integrated yield analysis[C]//Advanced Semiconductor Manufacturing Conference and Workshop, 1997. Cambridge, MA, USA, IEEE/SEMI. IEEE, 1997: 67-75.

CHIEN C, HSU C, CHEN P, et al. Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence[J]. Flexible Services and Manufacturing Journal, 2013, 25(3): 367-388. DOI: 367

CHIEN C F, CHEN Y J, WU J Z. Big data analytics for modeling WAT parameter variation induced by process tool in semiconductor manufacturing and empirical study[C]//Winter Simulation Conference (WSC), 2016. IEEE, Hyderabad, India, 2016: 2512-2522.

CHIEN C, WANG W, CHENG J, et al. Data mining for yield enhancement in semiconductor manufacturing and an empirical study[J]. Expert Systems With Applications, 2007, 33(1): 192-198. DOI: 192

ZHANG B, PAN W, ZHENG Y, et al. A fully automated large-scale addressable test chip design with high reliability[J]. european conference on circuit theory and design, 2011: 61-64. DOI: 10.1109/ECCTD.2011.6043609.

BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1):5-32. DOI: 5

BREIMAN L. Bagging predictors[J]. Machine Learning, 1996, 24(2):123-140. DOI: 123

WEISS S M, BASEMAN R J, TIPU F, et al. Rule-based data mining for yield improvement in semiconductor manufacturing[J]. Applied Intelligence, 2010, 33(3): 318-329. DOI: 318

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