OATT: Outlier-Oriented Alternative Testing and Post-Manufacture Tuning of Analog/Mixed-Signal Circuits

Abstract

Modern analog mixed-signal (AMS) devices manufactured in advanced CMOS processes pose significant testing and post-manufacture tuning challenges. Measurement of the specifications of AMS components is generally difficult as this requires the use of a range of dedicated tests while defect-based testing on the other hand, requires extensive defect simulations that are compute-intensive. To overcome these limitations, this research proposes OATT; a testing and post-manufacture tuning approach for AMS circuits that is designed to stress the performance of the device under test (DUT), formalize a statistical (multidimensional Gaussian) distribution of the expected response of known “good” devices (inliers), and use test limits grounded in theoretical statistics to classify all out-of-distribution devices (outliers) as “bad.” It is an alternative test approach in that it does not explicitly target simulation of defect mechanisms. Tuning is performed to transform individual outlier DUT responses to those resembling inlier devices by modulating hardware tuning knobs, such as bias voltages and currents, using a reinforcement learning algorithm. Circuit simulations and hardware results demonstrate the viability and efficiency of the proposed approach.