Post-Silicon Receiver Equalization Metamodeling by Artificial Neural Networks

• Published in: IEEE transactions on computer-aided design of integrated circuits and systems Vol. 38; no. 4; pp. 733 - 740
• Main Authors: Rangel-Patino, Francisco Elias, Rayas-Sanchez, Jose Ernesto, Viveros-Wacher, Andres, Chavez-Hurtado, Jose Luis, Vega-Ochoa, Edgar Andrei, Hakim, Nagib
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural network (ANN); Artificial neural networks; Complexity; Computer simulation; Data models; Design of experiments; Equalization; high-speed input/output (HSIO); Integrated circuit modeling; Machine learning; Metamodels; Neural networks; Platforms; post-silicon validation; receiver; Receivers; Silicon; simulation; system margining; Tuning; Variations
• ISSN: 0278-0070; 1937-4151
• DOI: 10.1109/TCAD.2018.2834403

As microprocessor design scales to the 10-nm technology and beyond, traditional pre- and post-silicon validation techniques are unsuitable to get a full system functional coverage. Physical complexity and extreme technology process variations severely limits the effectiveness and reliability of presilicon validation techniques. This scenario imposes the need of sophisticated post-silicon validation approaches to consider complex electromagnetic phenomena and large manufacturing fluctuations observed in actual physical platforms. One of the major challenges in electrical validation of high-speed input/output (HSIO) links in modern computer platforms lies in the physical layer (PHY) tuning process, where equalization techniques are used to cancel undesired effects induced by the channels. Current industrial practices for PHY tuning in HSIO links are very time consuming since they require massive laboratory measurements. An alternative is to use machine learning techniques to model the PHY, and then perform equalization using the resultant surrogate model. In this paper, a metamodeling approach based on neural networks is proposed to efficiently simulate the effects of a receiver equalizer PHY tuning settings. We use several design of experiments techniques to find a neural model capable of approximating the real system behavior without requiring a large amount of actual measurements. We evaluate the models performance by comparing with measured responses on a real server HSIO link.