The Neural Engineering of ATE

This paper describes a neural network-based software configuration implemented on a VXI automatic test equipment platform. The purpose of this unique software configuration, incorporating neural network and other artificial intelligence (AI) technologies, is to enhance ATE capability end efficiency by providing an intelligent interface for a variety of functions that are controlled or monitored by the software. This includes automated end user-directed control of the ATE end diagnostic strategy to streamline test sequences through the use of advanced diagnostic strategies. The use of Neural Engineering techniques are stressed which, in this context, foster the integration of diverse sensor technology capable of analyzing units under test (UUT) from different perspectives that provide new insight into static, dynamic, and historical UUT performance. Such methods can achieve greater accuracy in failure diagnosis and fault prediction; reduction in cannot duplicate (CND), retest-OK (RTOK) rates, and ambiguity group size; and improved confidence in performance testing that results in the determination of UUT ready for issue status. The hardware configuration of the ATE consists of an embedded 486 100 MHz PC controller and an instrument suite as follows: Power Supply, DMM, Digitizer, Counter/Timer, Digital I/O, Pulse Generator, Switching Matrix, Relay, Function Generator and Arbitrary Function Generator