Feb 26, 2023

[paper] Framework for FPGA Emulation of IC Designs

S. Herbst, G. Rutsch, W. Ecker and M. Horowitz
An Open-Source Framework for FPGA Emulation of Analog/Mixed-Signal Integrated Circuit Designs
in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
vol. 41, no. 7, pp. 2223-2236, July 2022
DOI: 10.1109/TCAD.2021.3102516

Abstract: This article presents an open-source framework for emulating mixed-signal chip designs on a field-programmable gate array (FPGA). It includes a Python-based synthesizable model generator for mixed-signal blocks (msdsl), a fixed-point and floating-point synthesizable SystemVerilog library for representing real numbers (svreal), and a Python-based tool that generates emulator control infrastructure and automates the FPGA build process (anasymod). The framework includes features for efficiently modeling analog dynamics, nonlinearity, and noise, often making use of compile-time caching to reduce the required computational resources of the FPGA. We demonstrate the framework’s generality by discussing three applications: 1) a high-speed link receiver (DragonPHY); 2) a firmware-controlled flyback converter; and 3) an NFC-powered chip. Our framework makes it easy to emulate these systems, while providing runtimes 2–3 orders of magnitude faster than CPU simulations with real-number functional models. FOSS framework, depicted in Fig. 1, consists of three tools: 1) msdsl ; 2) svreal; and 3) anasymod. All three tools released as open source can be installed either from GitHub or by using the Python package manager (pip).


FIG: Overview of FOSS AMS emulation framework. Analog models are described in Python and compiled into synthesizable SystemVerilog using msdsl, leveraging svreal to implement real-number operations. anasymod then wraps emulator control infrastructure around the DUT and automates EDA tools to produce an FPGA bitstream.






[review] SOI devices and their basic properties

Rudenko, T. E., A. N. Nazarov, and V. S. Lysenko
The advancement of silicon-on-insulator (SOI) devices and their basic properties
Semiconductor Physics, Quantum Electronics & Optoelectronics 23, no. 3 (2020)
DOI: 10.15407/spqeo23.03.227

* V. Lashkaryov Institute of Semiconductor Physics, NAS of Ukraine, 45, prospect Nauky, 03680 Kyiv, Ukraine

Abstract. Silicon-on-insulator (SOI) is the most promising present-day silicon technology. The use of SOI provides significant benefits over traditional bulk silicon technology in fabrication of many integrated circuits (ICs), and in particular, complementary metal-oxide-semiconductor (CMOS) ICs. It also allows extending the miniaturization of CMOS devices into the nanometer region. In this review paper, we briefly describe evolution of SOI technology and its main areas of application. The basic technological methods for fabrication of SOI wafers are presented. The principal advantages of SOI devices over bulk silicon devices are described. The types of SOI metal-oxide-semiconductor field-effect transistors (MOSFETs) and their basic electrical properties are considered. Keywords: silicon-on-insulator (SOI), metal-oxide-semiconductor field-effect transistor (MOSFET), multiple-gate transistor, ultra-thin-body SOI transistor, fully-depleted SOI transistor, interface coupling.

FIG: Equivalent capacitance circuit of the long-channel bulk / PD SOI MOSFET (a), and FD SOI MOSFET (b). Subthreshold characteristics of FD SOI MOSFET and bulk / PD SOI MOSFET (c).




[paper] Fast and Expandable ANN-Based Extraction

Jeong, HyunJoon, SangMin Woo, JinYoung Choi, HyungMin Cho, Yohan Kim,
Jeong-Taek Kong, and SoYoung Kim
Fast and Expandable ANN-Based Compact Model and Parameter Extraction for Emerging Transistors IEEE Journal of the Electron Devices Society (2023)
DOI 10.1109/JEDS.2023.3246477

Abstract: In this paper, we present a fast and expandable artificial neural network (ANN)-based compact model and parameter extraction flow to replace the existing complicated compact model implementation and model parameter extraction (MPE) method. In addition to nanosheet FETs (NSFETs), our published ANN based compact modeling framework is easily extended to negative capacitance NSFETs (NC-NSFETs), which are attracting attention as next-generation devices. Each device is designed using a technology computer-aided design (TCAD) simulator. Using device structure parameters, temperature, and channel doping depth as input variables, we construct a dataset of electrical properties used for machine learning (ML)-based modeling. The accuracy of predicting device electrical characteristics with the proposed ANN-based compact model is less than a 1% error compared to TCAD, and simulation results of digital and analog circuits using the proposed compact model show less than a 3% error. This allows the ANN-based modeling framework to achieve accurate DC, AC, and transient simulations without restrictions on device technology. In particular, temperature and process variables such as channel doping depth, which are not defined in the compact model parameters, are easily added to the previously presented five key parameters. Instead of conventional complex compact modeling and MPE work, we propose a method to create fast, accurate, flexible, and expandable ML-based Verilog-A SPICE models with design technology co-optimization (DTCO) capabilities.


Fig A: Conventional model parameter extraction flow

Fig B: The proposed ANN-based model parameter extraction flow

Acknowledgments: We thank the reviewers for improving the contents of the paper. This work was supported by an Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No.2021-0- 00754, Software Systems for AI Semiconductor Design) and by a National Research Foundation of Korea grant funded by the Korean government (MISP) (NRF-2020R1A2C1011831). The EDA tool was supported by the IC Design Education Center (IDEC), Korea

Feb 24, 2023

[ElectronicsB2B ] #TSMC To Build Second #semi #Foundry In #Japan https://t.co/v8BTcb97vh https://t.co/rvvCz2MTgY



from Twitter https://twitter.com/wladek60

February 24, 2023 at 05:17PM
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Feb 23, 2023

[C4P] FSiC 2023


The 2023 Free Silicon Conference (FSiC)
will take place in Paris (Sorbonne)
on July 10-12, 2023 (Monday to Wednesday)

This event will build on top of the past FSiC2019 and FSiC2022 editions. The conference will connect experts and enthusiasts who want to build a complete Free and Open Source CAD ecosystem for designing analog and digital integrated circuits. The conference will cover the full spectrum of the design process, from system architecture, to layout and verification. After the daily talks, the discussion will continue until late in an informal and relaxed atmosphere.

Participation to the conference is free of charge, but the attendance must be reserved per email at fsic2023 'at' f-si.org. Details will be announced on this page and over the mastodon channel. The slides and the video recordings of the talks will be published on our website.

Discussion topics of the 2023 Free Silicon Conference (FSiC) 
  • High-level design
  • Hardware security
  • On-going FOSS silicon projects
  • Memories
  • Foundries and free PDKs
  • Transistor Compact/SPICE/Verlog-A modeling
  • Place-and-route tools
  • Parasitic extraction
  • Design rule checking
  • Schematic editors
  • Photonics
  • Sustainability
Submission: For proposing a talk, please submit a title and a short summary at
fsic2023 'at' f-si.org.

FSiC Organizing Committee