Quantum-based systems, such as quantum computers and quantum sensors, typically require a cryogenic electrical interface, which can be conveniently implemented using CMOS integrated circuits operating at cryogenic temperatures (cryo-CMOS). Reliable simulation models are required to design complex circuits, but CMOS transistor electrical characteristics at cryogenic temperatures substantially deviate from the behavior at room temperature, and no standard physics-based model exists for cryo-CMOS devices. To circumvent those limitations, this paper proposes the use of Artificial Neural Networks (ANN) and an associated training (extraction) procedure that automatically generates cryo-CMOS device models directly from experimental data. A device model for the DC characteristics of 40-nm CMOS transistors over a wide range of bias conditions, device geometries and temperatures from 4 K to 300 K has been generated and used to simulate voltage-reference circuits over a wide temperature range (4 K - 300 K). The potential application to dynamic/high-frequency circuits is demonstrated by enhancing the basic model with ANN-based nonlinear multi-terminal capacitive elements to simulate a ring oscillator. Preliminary results showing a good match between simulations and experiments demonstrate the feasibility and practicality of the proposed approach.
|Title of host publication||IEEE 14th Workshop on Low Temperature Electronics, WOLTE 2021 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Publication status||Published - 2021|
|Event||14th IEEE Workshop on Low Temperature Electronics, WOLTE 2021 - Virtual, Online, Italy|
Duration: 12 Apr 2021 → 16 Apr 2021
|Name||IEEE 14th Workshop on Low Temperature Electronics, WOLTE 2021 - Proceedings|
|Conference||14th IEEE Workshop on Low Temperature Electronics, WOLTE 2021|
|Period||12/04/21 → 16/04/21|
Bibliographical noteFunding Information:
ACKNOWLEDGMENTS The authors thank the legal teams of the TU Delft and Keysight Technologies, and Keysight Laboratories and Keysight Quantum Engineering Solutions organizations for encouragement and support. The authors at TU Delft would like to thank Intel for funding.
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Copyright 2021 Elsevier B.V., All rights reserved.
- CMOS integrated circuits
- quantum computing
- quantum sensing
- semiconductor device models