Machine learning application for silicon photonics transceiver testing

Woosung Kim; Yeoh Hoe Seng; Yi-Shing Chang; Suohai Mei

doi:10.1117/12.2320167

7 September 2018 Machine learning application for silicon photonics transceiver testing

Woosung Kim, Yeoh Hoe Seng, Yi-Shing Chang, Suohai Mei

Proceedings Volume 10751, Optics and Photonics for Information Processing XII; 107510E (2018) https://doi.org/10.1117/12.2320167
Event: SPIE Optical Engineering + Applications, 2018, San Diego, California, United States

Abstract

Optical transceivers often require multiple corner case test conditions in order to meet multiple source agreement (MSA) specs. Typically, some tests need to be applied under multiple temperatures using temperature controller, resulting in extensive test time and high manufacturing test cost. In this paper, we introduce machine learning based test methodology for silicon photonics transceiver manufacturing test with large percentage (>90%) of test time reduction. In order to reduce test time, the desire is to test at one temperature corner and apply machine learning techniques to eliminate other temperature corners. We complied wide range of data set from various prerequisite tests and target test data at temperature No.1 as input data set. Target test at temperature No.2 is employed for supervised learning prediction. For production implementation simplicity, we used linear regression model with Tikhonov regularization [1] and reached R2>0.97 of predicted value correlation with physical measurement value.

Citation Download Citation

Woosung Kim, Yeoh Hoe Seng, Yi-Shing Chang, and Suohai Mei "Machine learning application for silicon photonics transceiver testing", Proc. SPIE 10751, Optics and Photonics for Information Processing XII, 107510E (7 September 2018); https://doi.org/10.1117/12.2320167

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