Exploiting Defect Clustering Information in VLSI Testing

在 VLSI 测试中利用缺陷聚类信息

• 批准号: 9208929
• 负责人: Adit Singh
• 金额: $ 14.01万
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-10-01 至 1996-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9208929&HistoricalAwards=false
• 关键词: Exploiting; Defect; Clustering; Information; VLSI

This project is investigating the use of information about defect clustering on wafers to make yield predictions for individual dies based on test results for neighboring dies. The hypothesis is that defects on a wafer are clustered, thus most good dies are close to other good ones and most defective dies are close to others. Clustering information can then be used for targeting particular tests to individual dies on the wafer. Simulations using wafer defect distribution data from the published literature is being employed to assess the viability of the approach. Accurate models to predict defect levels attainable are being developed and strategies for testing dies on the wafer are being determined.

该项目正在研究如何利用晶圆上缺陷聚集的信息，根据相邻芯片的测试结果来预测单个芯片的良率。 假设晶圆上的缺陷是聚集的，因此大多数好的芯片与其他好的芯片接近，而大多数有缺陷的芯片与其他芯片接近。 然后，聚类信息可用于针对晶圆上的各个芯片进行特定测试。 使用已发表文献中的晶圆缺陷分布数据进行模拟来评估该方法的可行性。 正在开发预测可达到的缺陷水平的准确模型，并确定测试晶圆上芯片的策略。