Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement defect solutions. In one aspect, a method includes receiving a log file and testing results generated from a code base for an application; processing the log file through a pattern-mining algorithm to determine a usage pattern of code modules within the code base; clustering defects from the testing results based on a respective functionality of the application reported within each of the defects; generating testing prioritizations for test cases for the application by assigning weightages to the test cases based on the clusters of defects and the usage pattern of the code modules within the code base; sequencing a set of the test cases based on the test prioritizations; and transmitting the sequence to a test execution engine.

In one embodiment, a method of operating a computational system to evaluate a device under test, where the device under test is operable to receive a digital code input and output in response a corresponding output. The method injects a plurality of simulated faults into a pre-silicon model of the device under test. For each injected simulated fault, the method inputs a plurality of digital codes to the model. For each input digital code, the method selectively stores the input digital code if a difference, between a corresponding output for the input digital code and a no-fault output for the input, exceeds a predetermined threshold value.

Disclosed herein are exemplary methods, apparatus, and systems for performing timing-aware automatic test pattern generation (ATPG) that can be used, for example, to improve the quality of a test set generated for detecting delay defects or holding time defects. In certain embodiments, timing information derived from various sources (e.g. from Standard Delay Format (SDF) files) is integrated into an ATPG tool. The timing information can be used to guide the test generator to detect the faults through certain paths (e.g., paths having a selected length, or range of lengths, such as the longest or shortest paths). To avoid propagating the faults through similar paths repeatedly, a weighted random method can be used to improve the path coverage during test generation. Experimental results show that significant test quality improvement can be achieved when applying embodiments of timing-aware ATPG to industrial designs.

Provided test method and apparatus of communication chip, device and medium. The test method of communication chip includes receiving end test method and transmitting end test method. The receiving end test method of the communication chip includes: an idle time slot of the receiving end of the communication chip is detected in a running process of the communication chip; a test vector is generated, and a standard result corresponding to the test vector is generated; a data frame containing the test vector is constructed, and the data frame is sent to the receiving end of the communication chip in the idle time slot to enable the receiving end of the communication chip to process the data frame; and a chip processing result uploaded by the receiving end of the communication chip is received, and the standard result is compared with the chip processing result.

Systems and methods for generating defect criticality are disclosed. Such systems and methods may include identifying defect results including a defect and a defect location. Such systems and methods may include receiving fault test recipes configured to test potential faults at a plurality of testing locations. Such systems and methods may include identifying a plurality of N-detect parameters based on a countable number of times the fault test recipes are configured to test a potential fault. Such systems and methods may include determining a plurality of weighting parameters based on the plurality of N-detect parameters. Such systems and methods may include generating the defect criticality for the defect based on a proximity between the plurality of testing locations and the defect location and the plurality of weighting.

Example embodiments are disclosed of systems and methods for predicting failure probabilities of future product tests of a testing sequence based on outcomes of prior tests. Predictions are made by a machine-learning-based model (MLM) trained with a set of test-result sequence records (TRSRs) including test values and pass/fail indicators (PRIs) of completed tests. Within training epochs over the set, iterations are carried out over each TRSR. Each iteration involves sub-iterations carried out successively over test results of the TRSR. Each sub-iteration involves (i) inputting to the MLM values of a given test and those of tests earlier in the sequence while masking those later in the sequence, (ii) computing probabilities of test failures for the masked tests found later in the sequence than the given test, and (iii) applying the PFIs of test results later in the sequence than the given test as ground-truths to update parameters of the MLM.

An integrated circuit (IC) test engine generates N-cycle at-speed test patterns for testing for candidate faults and/or defects of a first set of transition faults and/or defects of an IC design. A diagnostics engine that receives test result data characterizing application of the N-cycle at-speed test patterns to a fabricated IC chip based on the IC design by an ATE, in which the test result data includes a set of miscompare values characterizing a difference between an expected result and a result measured by the ATE for a given N-cycle at-speed test pattern. The diagnostics engine employs a fault simulator to fault-simulate the N-cycle at-speed test patterns against a fault model that includes a first set of transition faults and/or defects and fault-simulate a subset of the N-cycle at-speed test patterns against a fault model that includes multicycle transition faults and/or defects utilizing sim-shifting.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement determine defect solutions. In one aspect, a method includes the actions of receiving a log file that includes log records generated from a code base; processing the log file through a pattern mining algorithm to determine a usage pattern; generating a graphical representation based on an analysis of the usage pattern; processing the graphical representation through a machine learning algorithm to select a set of test cases from a plurality of test cases for the code base and to assign a priority value to each of the selected test cases; sequencing the set of test cases based on the priority values; and transmitting the sequenced set of test cases to a test execution engine.

Embodiments of the present invention provide systems and methods for generating a set of test cases using a base test program. The base test program may be used as both a functional drive and as a performance measuring test case. From the base test program, additional key and value pairs may be added to the base test program to force specific test scenarios.

An electronic device test database generating method, comprising: (a) acquiring cell layout information of a target electronic device; (b) generating possible defect location information of the target electronic device according to the cell layout information, wherein the possible defect location information comprises at least one possible defect location of the target electronic device; (c) testing the target electronic device according to the possible defect location information to generate a testing result; and (d) generating an electronic device test database according to the testing result.