Tutorials
A tutorial on simulating Python defined ML device models in Xyce, an open source SPICE circuit simulator
Authors: Paul Kuberry, Ting Mei, Andy Huang, Eric Keiter, Biliana Paskaleva, Thomas Buchheit, Pavel Bochev & Shahed Reza
Sandia National Laboratories, Albuquerque, New Mexico, USA
Presenter: Dr. Shahed Reza
Xyce is an open source, highly accurate, and massively parallel SPICE circuit simulator developed at Sandia National Laboratories. The Xyce Python Model Interpreter (Xyce-PyMi), also developed at Sandia, enables the execution of devices with behavior defined in Python as part of a larger circuit simulation using Xyce. This allows the circuit designers to rapidly generate models from data and to evaluate circuit performances in a production circuit simulator, leading to a robust and higher confidence design.
In this tutorial, starting with an introduction to Xyce, the audience will be guided through installation of Xyce-PyMi, development of an electrical device defined in Python, and will be given resources for use in developing more complicated data-driven device models using Python packages such as TensorFlow.
Shahed Reza is an R&D manager at Sandia National Laboratories. His department is involved in research activities on physics-based device and subsystem modelling methodologies. Prior to joining Sandia, he worked at multiple companies in different R&D positions. He was an RF/MW device modeling expert at Raytheon’s state of the art Gallium nitride (GaN) Fabrication facility. He worked as an analog RF and microwave circuit designer at Keysight (formerly Agilent) technologies. He was a Research Scientist at Philips Medical Systems, where he conducted research on image guided noise tomography and RF probes for Magnetic Resonance imaging. He was a Silicon design lead at the Imaging and printing Division of Hewlett-Packard.
Dr. Reza has eight US patents and over 60 technical publications in peer reviewed journals and conferences. He earned his Ph.D. in Electrical Engineering from the University of Florida. The topic of his doctoral research was flicker and Generation-recombination noise properties of carbon nanotubes and silicon nanowires.
Design Automation of Analog and Mixed-Signal Circuits Using Artificial Neural Networks
Authors:
Nuno Lourenço1, Nuno Horta1 & José M. de la Rosa2
1Instituto de Telecomunicações (IT), PT, 2Institute of Microelectronics of Seville, IMSE-CNM (CSIC/University of Seville), ES
Presenter: Dr. de la Rosa
This tutorial shows how to use Artificial Neural Networks (ANNs) for the optimization and automated design of analog and mixed-signal ICs. A survey of main (conventional) design methods and EDA tools is given to show the pros and cons of the prior art, as a motivation towards using ANNs as optimization engines. A step-by-step methodology is given to explain the key aspects to consider in the presented approach, such as dataset preparation, ANN modeling and optimization, and their application to a given design problem. Several case studies are considered as demonstration vehicles and examples at different hierarchy levels of analog IC design. The first one is based on the use of ANNs and spice-like simulators to optimize amplifiers (AIDAsoft) and the second one consists of combining ANNs and behavioral simulation (SIMSIDES) for the high-level sizing of Sigma-Delta Modulators (ΣΔMs). The tutorial is addressed to a general audience interested in learning the fundamentals and practical considerations of using ANNs as optimization engines for the automated design of analog and mixed-signal ICs.
José M. de la Rosa (Fellow, IEEE) received the M.S. degree in Physics in 1993 and the Ph.D. degree in Microelectronics in 2000, both from the University of Seville, Spain. Since 1993 he has been working at the Institute of Microelectronics of Seville (IMSE), which is its turn part of the Spanish Microelectronics Center (CNM) of the Spanish National Council of Scientific Research (CSIC). He is presently the vice-director of IMSE and he is also a Full Professor at the Dept. of Electronics and Electromagnetism of the University of Seville.
His main research interests are in the field of analog and mixed-signal integrated circuits, especially high-performance (sigma-delta) data converters, including analysis, behavioral modeling, design and design automation of such circuits. In these topics, Dr. de la Rosa has participated in a number of Spanish and European research and industrial projects, and has co-authored over 260 international publications, including journal and conference papers, book chapters and the books Systematic Design of CMOS Switched-Current Bandpass Sigma-Delta Modulators for Digital Communication Chips (Kluwer, 2002), CMOS Cascade Sigma-Delta Modulators for Sensors and Telecom: Error Analysis and Practical Design (Springer, 2006), Nanometer CMOS Sigma-Delta Modulators for Software Defined Radio (Springer, 2011) and CMOS Sigma-Delta Converters: Practical Design Guide (Wiley-IEEE Press, 2013, 2nd Edition, 2018). He is in the World’s Top 2% Scientists List from Stanford University (editions 2019, 2020 and 2022).
Dr. de la Rosa is an IEEE Fellow and member of the IEEE Circuits and Systems Society (CASS) and the IEEE Solid-State Circuits Society (SSCS). He served as a Distinguished Lecturer of IEEE-CASS (term 2017-2018), and as Chair of the Spain Chapter of IEEE-CASS during the term 2016-2017. He was at the front of the Editorial Board of IEEE Transactions on Circuits and Systems II: Express Briefs, where he served as Deputy Editor-in-Chief since 2016 to 2019, and as Editor-in-Chief in the term 2020-2021. He is a member of the TechRxiv Editorial Advisory Board since 2022. He also served as Associate Editor for IEEE Transactions on Circuits and Systems I: Regular Papers, where he received the 2012-2013 Best Associate Editor Award and was Guest Editor for the Special Issue on the Custom Integrated Circuits Conference (CICC) in 2013 and 2014. He served as Guest Editor of the Special Issue of the IEEE J. on Emerging and Selected Topics in Circuits and Systems on Next-Generation Delta-Sigma Converters. He is a member of the Analog Signal Processing Technical Committee of IEEE-CASS and of the Steering Committee of IEEE MWSCAS. He has also been involved in the organizing and technical committees of diverse international conferences, among others IEEE ISCAS, IEEE MWSCAS, IEEE ICECS, IEEE LASCAS, IFIP/IEEE VLSI-SoC, DATE and ESSCIRC. He served as TPC chair of IEEE MWSCAS 2012, IEEE ICECS 2012, IEEE LASCAS 2015 and IEEE ISICAS (2018, 2019). He has been a member of the Executive Committee of the IEEE Spain Section (terms 2014-2015 and 2016-2017), where he served as Membership Development Officer during the term 2016-2017. He has been elected as Member-at-Large of the IEEE-CASS BoG for the 2023-2025 term.
Contact data and more details about the speaker available at www.imse-cnm.csic.es/~jrosa.
ADE Advanced Optimization: Open framework for ML/AI algorithms integration and visualization
Authors:
Valery Fouron, Ron Pongratz, Marat Yakupov and Klaus Cerny
Cadence
Finding the optimum design parameters in analog circuit design is a tedious and lengthy process, requiring a large number of simulations across PVT corners. To ease this process the Analog Design Environment (ADE) will provide the capability to run a trained model users can plug in via an API. The impact of the design optimization capabilities can be monitored real time by the designer through design space visualization capabilities in ADE. In this tutorial we will introduce you to these new capabilities of this open framework called “ADE Advanced Optimization” along with a demo based on a design example.