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A machine learning approach to model the impact of line edge roughness on gate-all-around nanowire FETs while reducing the carbon footprint

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  • Antonio García-Loureiro
  • Natalia Seoane
  • Julián G Fernández
  • Enrique Comesaña
  • Juan C Pichel

Abstract

The performance and reliability of semiconductor devices scaled down to the sub-nanometer regime are being seriously affected by process-induced variability. To properly assess the impact of the different sources of fluctuations, such as line edge roughness (LER), statistical analyses involving large samples of device configurations are needed. The computational cost of such studies can be very high if 3D advanced simulation tools (TCAD) that include quantum effects are used. In this work, we present a machine learning approach to model the impact of LER on two gate-all-around nanowire FETs that is able to dramatically decrease the computational effort, thus reducing the carbon footprint of the study, while obtaining great accuracy. Finally, we demonstrate that transfer learning techniques can decrease the computing cost even further, being the carbon footprint of the study just 0.18 g of CO2 (whereas a single device TCAD study can produce up to 2.6 kg of CO2), while obtaining coefficient of determination values larger than 0.985 when using only a 10% of the input samples.

Suggested Citation

  • Antonio García-Loureiro & Natalia Seoane & Julián G Fernández & Enrique Comesaña & Juan C Pichel, 2023. "A machine learning approach to model the impact of line edge roughness on gate-all-around nanowire FETs while reducing the carbon footprint," PLOS ONE, Public Library of Science, vol. 18(7), pages 1-17, July.
    • Handle: RePEc:plo:pone00:0288964
    DOI: 10.1371/journal.pone.0288964
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    References listed on IDEAS

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    1. Wei-Yin Loh, 2014. "Fifty Years of Classification and Regression Trees," International Statistical Review, International Statistical Institute, vol. 82(3), pages 329-348, December.
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