Logo image
Real-Time Abnormality Detection of Automatic Test Equipment by Cloud-Based Test Yield Monitoring and Analysis
Thesis

Real-Time Abnormality Detection of Automatic Test Equipment by Cloud-Based Test Yield Monitoring and Analysis

Chen. Tsai Huang
Masters, 國立清華大學, 電機工程學系
2015

Abstract

自動化測試機台 良率分析 automatic test equipment yield analysis
An automatic test equipment (ATE) is developed to test the numerous integrated circuits (ICs) automatically. Some ATE issues occur during the testing and lead to the difference of the true yield and the test yield. It is a waste of time to re-test the whole lot of ICs if the deviation of yield caused by an ATE issue occurs during the testing. A method of detecting the ATE issues in real-time is proposed for saving the re-test time. A data analyzer called ATE Variance Detector (ATEVD) monitors the test yield in the data that upload from the ATEs. If an abnormality of test yield exists in the test data, the ATEVD notifies the engineer about the abnormality. We established a web server that contains the ATEVD and a data generator, which is developed to generate the test data of the ATEs. We verified the ATEVD of the ability to detect the abnormality of the test yield in the test data, and proved that the ATEVD is able to detect the ATE issue through monitoring the test data. We also give the experiments about how the detection rule settings affect the detection properties. Finally, we proved that the ATEVD is able to detect the abnormality of the test yield by monitoring the test data in a short time after the ATE issue occurs. Therefore, the engineer that noticed of the ATE issues is able to halt the testing and fix the ATE issues. A great deal of re-testing time is hence saved.

Metrics

1 Record Views

Details

Logo image