Test costs for large industrial designs increase rapidly in recent years. On-chip test compression hardware has become a pragmatic technology to cut down the overall test costs by reducing the test data volume. Determining the input and output channel counts of test compression hardware that results in minimum test data volume is thus a critical issue. In this article, efficient methods to estimate test pattern counts for an extensive range of input/output counts are developed. These methods require only a small number of ATPG runs. The estimation results can then be utilized to determine the test data volume for each input/output configuration. The configuration with the estimated lowest test data volume thus can be determined. The pattern count results of each configuration for a design can also be used to determine the best suitable configuration when the design is to be embedded in an SoC system.

A novel method to efficiently and accurately prognosticate the pattern count at different input compression ratios with the Embedded Deterministic Test (EDT) compression technology is proposed. With this method the total ATPG run time can be significantly reduced compared to the currently used trial-and-error method.

Over the past two decades, test data compression has become a de facto technology used in large industrial designs to reduce the overall test cost. During DFT planning, it is very important to understand the impact of using different numbers of input/output channels on test coverage, test cycles, and test data volume. In this paper, an efficient method to estimate the test data volume with different input channel counts using the Embedded Deterministic Test (EDT) compression technology is proposed. The results can then be used to quickly determine the scan configuration that results in the least or near least test data volume. With this method, the total ATPG run time can be reduced by a factor of more than 10X compared to the currently used trial-and-error method.

As the complexity of industrial integrated circuits continue to increase rapidly, test data compression has now become a de facto technology for large designs to reduce the overall test cost. During the design for test (DFT) planning, it is critical to understand the impact of using different numbers of input/output test channels on test coverage, test cycles, and test data volume. In this paper, two approaches to predict the test pattern counts and test data volumes with different input channel counts are presented, one with the compression tool able to generate channel-scaling patterns and the other without this capability. The results can be used to determine the scan test configuration that results in the smallest or near smallest test data volume. Experiments on industrial circuits show that the average error rates of pattern count prediction for most circuits are less than 10% for both approaches. The error rates of the predicted smallest data volumes are all less than 3.5%. The total ATPG run time can be reduced by a factor of more than 10X compared to the currently used trial-and-error approach.

With the increase in design complexity and test data volume, compressed tests together with on-chip test decompression hardware such as Embedded Deterministic Test (EDTTM) are widely used in industry in order to reduce test cost. One of the challenges of such Design-for-Test (DFT) technology is to determine a set of optimal parameters such as the number of scan chains, scan channels, power budget, etc. such that it can reach the highest test coverage with a minimum amount of test data volume whilst satisfying various other constraints. To achieve the optimal compression configuration quickly, in this work deep learning technology based on Tensorflow is explored to estimate the test coverage and the data volume for a design when employing EDT under a given set of circuit parameters. Based on the estimated data, the optimal test architecture is also predicted, yielding a more efficient approach compared to the currently used trial-and-error methods. To demonstrate the advantages of our deep learning approach over the currently used utility, we present experimental data for eight industrial designs.