Prediction of multi-dimensional spatial variation data via Bayesian tensor completion.
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems(2019)
摘要
This paper presents a multi-dimensional computational method to predict the spatial variation data inside and across multiple dies of a wafer. This technique is based on tensor computation. A tensor is a high-dimensional generalization of a matrix or a vector. By exploiting the hidden low-rank property of a high-dimensional data array, the large amount of unknown variation testing data may be predicted from a few random measurement samples. The tensor rank, which decides the complexity of a tensor representation, is decided by an available variational Bayesian approach. Our approach is validated by a practical chip testing data set, and it can be easily generalized to characterize the process variations of multiple wafers. Our approach is more efficient than the previous virtual probe techniques in terms of memory and computational cost when handling high-dimensional chip testing data.
更多查看译文
关键词
Testing,Probes,Semiconductor device measurement,Arrays,Bayes methods,Numerical models
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络