  In this paper, we propose a deep learning based performance testing framework
to minimize the number of required test modules while guaranteeing the accuracy
requirement, where a test module corresponds to a combination of one circuit
and one stimulus. First, we apply a deep neural network (DNN) to establish the
mapping from the response of the circuit under test (CUT) in each module to all
specifications to be tested. Then, the required test modules are selected by
solving a 0-1 integer programming problem. Finally, the predictions from the
selected test modules are combined by a DNN to form the specification
estimations. The simulation results validate the proposed approach in terms of
testing accuracy and cost.
