Cadence Design Systems, Inc.

David George Scott

Vivek Chickermane

Krishna Chakravadhanula

Brian Edward Foutz

Paul Alexander Cunningham

Steev Wilcox

Dale Meehl

Louis Christopher Milano

Faisal R Khoja

Systems and methods efficiently bring additional variables into a Pseudo-Random Pattern Generator ('PRPG') in the early cycles of an automatic test pattern generation ('ATPG') process without utilizing any additional hardware or control pins. Overscanning (e.g., scanning longer than the length of the longest channel) for some additional cycles brings in enough variables into the PRPG. Data corresponding to earlier cycles of the ATPG process is removed.

Method and system for improving efficiency of sequential test compression using overscan

Systems and methods disclosed herein provide for utilizing extra variables in the decompression equation set of an ATPG process for test patterns requiring an excess number of care bits than can be supported efficiently by the current hardware. An elastic interface is utilized between a tester and a decompressor network (e.g., sequential and combinational decompressors) in order to expand the test pattern length and/or the number of input variables. The systems and methods also provide care bits in early scan cycles of the ATPG process for sequential decompressors starting from a fixed state.

Elastic compression-optimizing tester bandwidth with compressed test stimuli using overscan and variable serialization

A method for identifying, by way of a genetic algorithm, test points to be inserted in an integrated circuit (IC) chip to improve the testability of the IC is described. The algorithm is particularly well suited for large circuit designs (several million gates) because it allows to simultaneous insert multiple additional test points at critical locations of the IC to gain supplemental controlability and/or observability and thereby eliminating the drawbacks associated with the single test point approach. To further improve performance, cost function gradient techniques are applied to guide the selection of potential test points for consideration by the algorithm. Fault simulation of random patterns is used to more accurately distinguish between random pattern testable and random resistant faults, and to provide a more accurate set of initial probabilities for the cost function calculations. The algorithm further identifies a reduced set of potential candidate test points according to a variety of criteria such as cluster roots, i.e., nodes in the IC having poor controlability at the outputs but good controlability at the inputs, by considering the inputs to the cluster roots as good test point candidates. The genetic algorithm makes it a prime candidate for implementation using parallel processing, wherein multiple computers are used to simultaneously evaluate potential solutions.

Method for identifying test points to optimize the testing of integrated circuits using a genetic algorithm

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Endwell, NY, United States of America

David George Scott