Modeling Emerging Technologies using Machine Learning: Challenges and Opportunities

Florian Klemme, Jannik Prinz, Victor M. Van Santen, Jorg Henkel, Hussam Amrouch

Research output: Contribution to journalConference articlepeer-review

20 Scopus citations

Abstract

Compact models of transistors act as the link between semiconductor technology and circuit design via circuit simulations. Unfortunately, compact model development and calibration is a challenging and time-intensive task, hindering rapid prototyping of a circuit (via circuit simulations) in emerging technologies. Moreover, foundries want to protect their confidential technology details to prevent reverse engineering. Hence, they limit access to compact transistor models of commercial technologies (e.g., with Non-Disclosure-Agreements). In this work, we propose Machine Learning (ML) to bridge the gap between early device measurements and later occurring compact model development. Our approach employs a Neural Network (NN) that captures the electrical response of a conventional FinFET transistor without knowledge of semiconductor physics. Additionally, our approach can be applied to emerging technologies, using Negative Capacitance FinFET (NC-FinFET) as an example for a (challenging to model) emerging technology. Inherently, the black-box nature of ML approaches keeps technology manufacturing details confidential. Furthermore, we show how using solely R2 score as our fitness function is insufficient and instead propose fitness based on key electrical characteristics or transistors like threshold voltage. Our NN-based transistor modeling can infer FinFET and NC-FinFET with an R2 score larger than 0.99 and transistor characteristics within 5% of experimental data.

Original languageEnglish
Article number9256420
JournalIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
Volume2020-November
DOIs
StatePublished - 2 Nov 2020
Externally publishedYes
Event39th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2020 - Virtual, San Diego, United States
Duration: 2 Nov 20205 Nov 2020

Keywords

  • Compact model
  • FinFET
  • Machine learning
  • Negative Capacitance FinFET
  • Neural Network
  • Transistor model

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