Defect-oriented testing and defective-part-level prediction

Abstract

After an integrated circuit (IC) design is complete, but before first silicon arrives from the manufacturing facility, the design team prepares a set of test patterns to isolate defective parts. Applying this test pattern set to every manufactured part reduces the fraction of defective parts erroneously sold to customers as defect-free parts. This fraction is referred to as the defect level (DL). However, many IC manufacturers quote defective part level, which is obtained by multiplying the defect level by one million to give the number of defective parts per million. Ideally, we could accurately estimate the defective part level by analyzing the circuit structure, the applied test-pattern set, and the manufacturing yield. If the expected defective part level exceeded some specified value, then either the test pattern set or (in extreme cases) the design could be modified to achieve adequate quality. Although the IC industry widely accepts stuck-at fault detection as a key test-quality figure of merit, it is nevertheless necessary to detect other defect types seen in real manufacturing environments. A defective-part-level model combined with a method for choosing test patterns that use site observation can predict defect levels in submicron ICs more accurately than simple stuck-at fault analysis.