Adaptive Confidence Interval-Based Alternate Test for Reliability Enhancement

The estimation model-based alternate test strategy for analog integration circuits (ICs) offers an effective way to reduce test costs. However, as a data-driven method, the estimation process of alternate test is invisible, leading to low test reliability. Hence, to address this problem, we propose an adaptive confidence interval-based alternate test (ACIT) to enhance the reliability of alternate test. Multiestimators are implemented to generate target parameters of each circuit synchronously. All estimations for the same sample are averaged to get the final result. The reliability of each final estimation is evaluated by comparing its adaptive confidence interval to the correlated parameter boundary. The proposed adaptive confidence interval is obtained from the variance of multioutputs and estimation-boundary distance. Estimations with confidence intervals crossing boundaries are identified as suspect results and returned to repeat testing by the conventional approach. The remaining results are classified as “pass” (entire confidence intervals within the qualified range) or “fail” (entire confidence intervals outside of the qualified range). Our approach is studied with simulation data and verified on commercial ICs. Results demonstrate that the ACIT can eliminate the misclassification circuits effectively by identifying unreliable estimations.