An Innovative Method for Thermal Characterization of Automotive Electronic Devices Based on CNNs

In this paper, a new approach for the thermal characterization of power electronic devices is introduced. The new method is based on cellular nonlinear networks (CNNs). By using the proposed technique, the thermal impedance of the device can be derived by processing a series of thermal maps, which can be acquired from a single experimental trial. In contrast, the most common technique (the delta drain-source voltage method) used for thermal characterization requires that the device is turned off after each single measure. The approach is validated on a power electronic device used in automotive applications, proving that the CNN-based method has an accuracy comparable with that of the delta drain-source voltage method but is more efficient in terms of time and human resources needed for device characterization.

[1]  Alyosha C. Molnar,et al.  Analysis of the interaction between the retinal ON and OFF channels using CNN-UM models , 2009, Int. J. Circuit Theory Appl..

[2]  Csaba Rekeczky,et al.  Topographic cellular active contour techniques: theory, implementations and comparisons , 2006, Int. J. Circuit Theory Appl..

[3]  Hossin Hosseinian,et al.  Power Electronics , 2020, 2020 27th International Conference on Mixed Design of Integrated Circuits and System (MIXDES).

[4]  Giovanni Costantini,et al.  Analogic CNN algorithm for estimating position and size of moving objects , 2004, Int. J. Circuit Theory Appl..

[5]  Marco Gilli,et al.  Template design methods for binary stable cellular neural networks , 2002, Int. J. Circuit Theory Appl..

[6]  P. Arena,et al.  Bio-Inspired Emergent Control of Locomotion Systems , 2004 .

[7]  V. Graziano-L. Guarrasi-A. Pavlin AN 1596-APPLICATION NOTE VIPower : HIGH SIDE DRIVERS FOR AUTOMOTIVE , 2002 .

[8]  Ronald Tetzlaff,et al.  Automated detection of a preseizure state: non‐linear EEG analysis in epilepsy by Cellular Nonlinear Networks and Volterra systems , 2006, Int. J. Circuit Theory Appl..

[9]  Ronald Tetzlaff,et al.  Toward an autonomous platform for spatio-temporal EEG-signal analysis based on cellular nonlinear networks , 2008 .

[10]  Ángel Rodríguez-Vázquez,et al.  ACE16k: the third generation of mixed-signal SIMD-CNN ACE chips toward VSoCs , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[11]  Leon O. Chua,et al.  Cellular neural networks: applications , 1988 .

[12]  Dr. Martin März,et al.  Thermal Modeling of Power-electronic Systems , 2000 .

[13]  Joos Vandewalle,et al.  Watermarking on CNN‐UM for image and video authentication , 2004, Int. J. Circuit Theory Appl..

[14]  Fernando Corinto,et al.  Comparison between the dynamic behaviour of Chua–Yang and full‐range cellular neural networks , 2003, Int. J. Circuit Theory Appl..

[15]  Fernando Corinto,et al.  Non‐linear coupled CNN models for multiscale image analysis , 2006, Int. J. Circuit Theory Appl..

[16]  Luigi Fortuna,et al.  Climbing obstacle in bio‐robots via CNN and adaptive attitude control , 2006, Int. J. Circuit Theory Appl..

[17]  Ernest Otto Doebelin,et al.  Measurement Systems Application and Design , 1966 .

[18]  Giovanni Egidio Pazienza,et al.  Robot vision with cellular neural networks: a practical implementation of new algorithms , 2007, Int. J. Circuit Theory Appl..

[19]  Tamás Roska,et al.  The CNN universal machine: an analogic array computer , 1993 .

[20]  Lin-Bao Yang,et al.  Cellular neural networks: theory , 1988 .