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CNN-Based Transistor Modeling Under Self-Heating Effects
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DOI：10.1109/TC.2026.3666827.png)
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Abstract
En 中文
Due to the steady shrinking of technology node sizes, Self-Heating Effect (SHE) has become at the forefront of reliability and longevity concerns. Without careful consideration of the increased temperatures, reliability effects such as aging will be underestimated, putting the transistor and circuit at risk. SHE challenges and concerns are all amplified at cryogenic temperatures, as at low temperatures, multiple physical changes take effect, like the significant decrease of thermal conductivity, carriers’ mobility increase, and lower heat dissipation due to the low ambient temperature. The study of SHE at cryogenic temperatures is especially important in applications like quantum computing and space electronics. To accurately anticipate the SHE on the transistor, physics-based transistor and semiconductor simulation tools, Technology CAD (TCAD), are used. TCAD tools offer accurate, physics-based transistor SHE results, which designers can use to make informed decisions. However, TCAD tools can only deliver accurate SHE results after a manual, time-intensive calibration process. This calibration can only be done for operating temperatures and transistor configurations with existing experimental data. The reliance on experimental data for calibration and the constant need for recalibration restrict the scope of the design process to only a limited subset of configurations. In this work, we propose the first data-driven surrogate Convolutional Neural Network (CNN)-based TCAD thermal model for SHE thermal profile and channel hotspot prediction. Our approach enables designers to sweep a huge amount of different operating temperatures without the dependency on available experimental data or the constant need for model recalibration, while achieving a thermal profile prediction accuracy of 99.91%, a thermal profile extrapolation accuracy of 96.88%, hotspot prediction average error of 0.127%, and hotspot extrapolation average error of 1.65%. All results are compared to calibrated TCAD thermal model simulation results. Our model also alleviates the high computational cost and low throughput associated with TCAD tools by offering a time speedup of more than 13000x for both prediction and extrapolation tasks.
Keywords:
TCAD simulation
self-heating
Machine learning
convolutional neural network
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