Teradyne has been granted a patent for techniques involving machine learning models to predict failing test results for devices under test (DUTs) in a test system. The model analyzes data from initial tests to identify patterns and predict outcomes for a different set of DUTs with similar features. GlobalData’s report on Teradyne gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Teradyne, Robot path planning was a key innovation area identified from patents. Teradyne's grant share as of February 2024 was 64%. Grant share is based on the ratio of number of grants to total number of patents.
Machine learning model for predicting failing test results
A recently granted patent (Publication Number: US11921598B2) discloses a method and system for predicting test failures in a test system using machine learning models. The patent claims describe a process where data from tests conducted on an initial set of devices under test (DUTs) is used to train a machine learning model. This model then predicts which tests will produce failing results for a different set of DUTs based on common features between the two sets. By identifying patterns from the results of the initial set that closely match patterns from the different set, the machine learning model can accurately predict test failures. The model operates by analyzing patterns associated with tests previously run on DUTs to make predictions, enabling efficient testing and outputting of test results based on the predicted failures.
Furthermore, the patent claims also detail the implementation of the machine learning model in a test system comprising test instruments and computer systems. The system utilizes the trained model to predict test failures for a specific DUT, control the testing process accordingly, and output test results based on the predicted failures. Additionally, the system includes provisions for analyzing test results to identify the cause of failures and determine whether retesting is necessary. The patent also mentions the use of a probe for test insertions during the prediction process, with prediction times being influenced by the complexity of the model and data set size. Overall, the patented method and system offer a sophisticated approach to predicting test failures in a test system using machine learning models, enhancing efficiency and accuracy in the testing process.
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