UC Santa Barbara

The manufacturing test process for a modern integrated circuit encounters excessively long test time and produces huge amount of test data. There is valuable information hidden in the test data about the device under test (DUT), far more than the binary go/no-go classification. Exploring the hidden correlations and patterns in the test data allows better understanding of the DUT and therefore leads to broad applications, such as test cost reduction and silicon characterization for discovering parametric variations and weak links

in the manufacturing process.

The first part of this dissertation proposes a methodology with supporting statistical learning algorithms for test time and cost reduction through exploiting both spatial and inter-test-item correlations in the test data. The proposed algorithm identifies inter-test-item correlations for removing costly and unnecessary test items from a test program. An integrated method further reduces test time by taking into account spatial correlations of test data across a wafer and maximizing the number of test items whose values can be predicted without measurement. A case study of a high-volume industrial device demonstrates that some test items can be identified for removal from the test program without compromising test quality and shows the significant reduction of test time.

In the second part of the dissertation, a framework for characterizing systematic variations and failures through exploring the hidden patterns of test data from multiple test stages is developed. The framework provides prediction of process variations with a fine resolution based on a limited number of probed process parameters, and extracts spatial patterns from both process parameters and production tests. A template matching technique exploits these spatial patterns to discover connections between process variations and failures detected by production tests. Experimental results demonstrate that the proposed framework reveals comprehensible and significant correlations in an industrial test dataset.

The third part of the dissertation describes a software toolbox dedicated to test data analytics developed in the course of this research. The toolbox provides flexible and scalable functions for parsing, processing, learning and display test data. The toolbox, which is released for non-commercial use, also provides examples and application programming interface for test data analysis.