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A Physics-Informed Adaptive Segmentation Fitting Method for Transient Thermal Test Analysis
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AI
DOI：10.1115/1.4070512.png)
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Abstract
En 中文
Transient thermal testing is a crucial technique for characterizing the thermal properties of semiconductor devices, but measurement noise often compromises its accuracy. Conventional square-root fitting (SRF) methods emphasize early-time details at the expense of global fidelity, whereas exponential fitting (EF) methods capture global behavior but sacrifice early-time physical detail. In this paper, we propose a physics-informed adaptive segmentation (PIAS) approach. By analyzing the differential characteristics of the thermal response, our method automatically identifies the physical transition region where heat flow evolves from quasi-one-dimensional to three-dimensional diffusion, thereby constraining the segmentation point without relying on empirical judgment. A composite objective function-incorporating global fitting error and derivative continuity constraints-is globally optimized via particle swarm optimization (PSO). Simulation and experimental results demonstrate that PIAS overcomes the inherent limitations of traditional techniques. Under strong noise conditions (signal-to-noise ratio as low as 45 dB), it outperforms leading commercial software by reducing the noise-induced error in total thermal resistance from 4.48% to just 0.15%, while achieving subsecond computation per fit. PIAS enables high-precision, fully automated transient thermal analysis with significant engineering relevance.
Keywords:
structure function, physics-informed fitting, adaptive segmentation, transient thermal testing
automated thermal analysis
Journal
J
IF:
2.3
Papers:
42
Citations:
2.0K