Speeding-Up Emerging Device Development Cycles by Generating Models via Machine-Learning directly from Electrical Measurements

In this work we present how an empirical compact model can be constructed directly from current measurement data by employing a machine learning technique. We demonstrate our approach on emerging dual-gated reconfigurable device test structures, which have been fabricated directly on an industrial 22 nm process. Variability of key figures of merit in reconfigurable field effect transistor test structures in the early development stage is analysed. The resulting model enables a fast adaptation to new geometries and provides simulation speed and convergence properties of a compact model, while being also flexibly adaptable to new technological iterations, thus speeding-up development cycles.

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Publication Details

Published
2024-09-09
DOI
https://doi.org/10.1109/esserc62670.2024.10719591
Citations
2
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.23

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Speeding-Up Emerging Device Development Cycles by Generating Models via Machine-Learning directly from Electrical Measurements

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2 citations
Machine Learning in Materials Science
0.23
article

Speeding-Up Emerging Device Development Cycles by Generating Models via Machine-Learning directly from Electrical Measurements

Jens Trommer, Thomas Mikolajick, Jan Hoentschel, A. Zeun, C. Metze, K. Li, Martin Reuter, Maximilian Drescher, V. Sessi, Viktor Havel, Klaus Hofmann, Giulio Galderisi, Stefan Slesazeck, Maik Simon, A.-S. Seidel, Niladri Bhattacharjee, M. Grothe, Sören Jansen, M. Zier, K. Ruttloff, Yang He
article en
2 citations

Abstract

In this work we present how an empirical compact model can be constructed directly from current measurement data by employing a machine learning technique. We demonstrate our approach on emerging dual-gated reconfigurable device test structures, which have been fabricated directly on an industrial 22 nm process. Variability of key figures of merit in reconfigurable field effect transistor test structures in the early development stage is analysed. The resulting model enables a fast adaptation to new geometries and provides simulation speed and convergence properties of a compact model, while being also flexibly adaptable to new technological iterations, thus speeding-up development cycles.

Technische Universität Darmstadt (DE), NaMLab (Germany) (DE), GlobalFoundries (Germany) (DE)
Ministry of Education
Industry, innovation and infrastructure
Openalex Percentile: Top 50%
Machine Learning in Materials Science
0.23
DOISourceOpenAlex
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