@article{li_jiao_chou_mayder_franzon_2020, title={CTLE Adaptation Using Deep Learning in High-speed SerDes Link}, ISSN={["2377-5726"]}, DOI={10.1109/ECTC32862.2020.00155}, abstractNote={To speed up a serial link simulation, it is critical to model the Serializer/Deserializer (SerDes) circuit behavior accurately. In this research, we focus on building a model for high-speed SerDes receiver CTLE adaptation behavior, which has a fast simulation speed and high-precision prediction. The proposed modeling method doesn’t need any substantial domain knowledge. Deep neural networks model will be used to mimic the behavior of the CTLE adaptation process in the receiver. The proposed modeling method shows high correlations with the CTLE adaptation codes.}, journal={2020 IEEE 70TH ELECTRONIC COMPONENTS AND TECHNOLOGY CONFERENCE (ECTC 2020)}, author={Li, Bowen and Jiao, Brandon and Chou, Chih-Hsun and Mayder, Romi and Franzon, Paul}, year={2020}, pages={952–955} }
 @article{li_jiao_chou_mayder_franzon_2020, title={Self-Evolution Cascade Deep Learning Model for High-Speed Receiver Adaptation}, volume={10}, ISSN={["2156-3985"]}, DOI={10.1109/TCPMT.2020.2992186}, abstractNote={The IBIS algorithmic modeling interface (IBIS-AMI) has become the standard methodology to model Serializer/Deserializer (SerDes) behavior for end-to-end high-speed serial link simulations. Meanwhile, machine learning (ML) techniques can mimic a black-box system behavior. This article proposes the self-evolution cascade deep learning (SCDL) model to show a parallel approach to effectively modeling adaptive SerDes behavior. Specifically, the proposed self-guide learning methodology uses its own failure experiences to optimize its future solution search according to the prediction of the receiver equalization adaptation trend. The proposed SCDL model can provide the high-correlation adaptation results, while the adaptation simulation time is much faster than conventional IBIS-AMI models.}, number={6}, journal={IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY}, author={Li, Bowen and Jiao, Brandon and Chou, Chih-Hsun and Mayder, Romi and Franzon, Paul}, year={2020}, month={Jun}, pages={1043–1053} }
 @inproceedings{li_franzon, title={Machine learning in physical design}, booktitle={Ieee conference on electrical performance of electronic packaging and}, author={Li, B. W. and Franzon, P. D.}, pages={147–149} }