3-D thermal models calibration by parametric dynamic compact thermal models |
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| Affiliation: | 1. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan I-20133, Italy;2. Department of Electrical Engineering and Information Technology, University Federico II, via Claudio 21, Naples 80125, Italy;1. Materials Center Leoben Forschung GmbH, Roseggerstraße 12, 8700 Leoben, Austria;2. Tridonic Jennersdorf GmbH, Technologiepark 10, 8380 Jennersdorf, Austria;3. Häusermann GmbH, Zitternberg 100, 3571 Gars am Kamp, Austria;4. Fraunhofer-Institut für Zuverlässigkeit und Mikrointegration, Gustav-Meyer-Allee 25, 13355 Berlin, Germany;1. Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin 300130, China;2. Institute of Project Management, Department of Leisure Industry Management, National Chin-Yi University of Technology, Taichung 41170, Taiwan;3. Department of Logistics Management, National Defense University, Taipei, 112, Taiwan;1. Department of Electrophysics, National Chiao Tung University, Hsinchu 300, Taiwan, ROC;2. Material and Design Engineering Division Engineering Center, SPIL, Taichung 400, Taiwan, ROC |
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| Abstract: | Detailed 3-D thermal models of electronic systems require the calibration of unknown parameters to accurately describe the experimental data, which is usually obtained by a least square optimization of the measured transient thermal response to a given set of power inputs. This paper presents an extremely efficient technique to perform the identification of boundary conditions, material thermal properties, and geometrical sizes, which is based on the adoption of the trust region algorithm in combination with parametric dynamic compact thermal models. The calibration of parameters of a Package-on-Package system is performed by a simulated experiment procedure to validate the applicability and accuracy of the proposed approach. It is shown that using parametric compact models allows for a significant reduction in computational effort in comparison to conventional brute-force optimization. The calibration robustness with respect to input degradation is examined by observing the variation in the extracted parameters at different levels of noise. |
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