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.

该项目正在研究基于邻近模具的测试结果对晶片上缺陷聚类的信息进行单个模具的产量预测。 假设是晶圆上的缺陷是聚集的，因此大多数良好的模具与其他好的模具接近，并且大多数有缺陷的模具与其他好的模具接近。 然后，可以使用聚类信息将特定测试定位到晶圆上的单个模具。 使用来自已发表文献的晶圆缺陷分布数据的仿真正在用于评估该方法的可行性。 正在开发预测可预测缺陷水平的准确模型，并且正在确定晶圆上测试模具的策略。