An artificial neural network approach for screening test escapes

In this paper we investigate the application of an artificial neural network (ANN) for screening test escapes. Specifically, we propose to train an autoencoder, an ANN, in an unsupervised way to fit the good chip population, i.e. using good chips only as the training set. The autoencoder is designed with both its input and output layers representing a set of features that characterize the test data of the chips under test, where we use the Euclidean distance between the values in the input and output layers as the cost function for training. Based on the trained autoencoder, if the test measurement of a query chip has an abnormally large value for the cost function, the chip is likely to be a test escape because it does not fit the characteristics of the good chip population captured by the model. We demonstrate that an autoencoder-based classification could achieve a higher detection rate for test escapes and a significant reduction in runtime and memory usage, compared with an SVM applied on the same features and some additional proximity features generated from multiple nonlinear transformations.