Process-aware circuit simulation using machine learning compact models for metal oxide semiconductor capacitors under illumination (Conference Presentation)

Zheng Kai Yang; Ya-Wen Ho; Tejender Rawat; Chung-Yuan Chang; Albert Lin

doi:10.1117/12.2632334

3 October 2022 Process-aware circuit simulation using machine learning compact models for metal oxide semiconductor capacitors under illumination (Conference Presentation)

Zheng Kai Yang, Ya-Wen Ho, Tejender Rawat, Chung-Yuan Chang, Albert Lin

Author Affiliations +

Proceedings Volume 12227, Applications of Machine Learning 2022; 122270M (2022) https://doi.org/10.1117/12.2632334
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

Machine learning (ML) based compact device modeling provides the opportunity for process-aware device modeling and thus process-aware circuit simulation. In contrast, incorporating semiconductor manufacturing parameters into compact models (CM) and subsequent circuit simulations using pure physics-based CM is difficult. We demonstrate process-aware circuit simulation where the effect of plasma treatment and thermal annealing can be directly reflected on the circuit output in SPICE transient simulation. The Verilog-A model input is V, frequency(f), area(A), and process conditions, i.e., plasma surface treatment (PST) and post-metal annealing (PMA). The MOSCAP capacitance-voltage (CV) characteristics under illumination are described by ML compact models.

Conference Presentation

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Zheng Kai Yang, Ya-Wen Ho, Tejender Rawat, Chung-Yuan Chang, and Albert Lin "Process-aware circuit simulation using machine learning compact models for metal oxide semiconductor capacitors under illumination (Conference Presentation)", Proc. SPIE 12227, Applications of Machine Learning 2022, 122270M (3 October 2022); https://doi.org/10.1117/12.2632334

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KEYWORDS

Device simulation

Machine learning

Capacitors

Metals

Oxides

Semiconductors

Instrument modeling

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