Per-Device Adaptive Test for Analog/RF Circuits Using Entropy-Based Process Monitoring
We present an adaptive test flow for mixed-signal circuits that aims at optimizing the test set on a per-device basis so that more test resources can be devoted to marginal devices while passing devices that are not marginal with less testing. Cumulative statistics of the process are monitored using a differential entropy-based approach and updated only when necessary. Thus, process shift is captured and continuously incorporated into the analysis. We also include provisions to identify potentially defective devices and test them more extensively since these devices do not conform to learned collective information. We conduct experiments on an low-noise amplifier circuit in simulations, and apply our techniques to production data of two distinct industrial circuits. Both the simulation results and the results on large-scale production data show that adaptive test provides the best tradeoff between test time and test quality as measured in terms of defective parts per million.
adaptive test process monitoring device level test adaptation growing neural networks nonlinear filtering support vector machine (svm)