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2024-10-19
A Novel Knowledge-Based Neural Network Approach to the Small-Signal Modeling of Packaged Diodes
By
Progress In Electromagnetics Research C, Vol. 149, 9-14, 2024
Abstract
This paper proposes a novel knowledge-based neural network approach that, in the absence of specific device SPICE models, can utilize the measured data of actual diode devices to map the existing diode coarse model to a more accurate package model through neural network mapping techniques, thereby achieving precise and efficient modeling of the small-signal characteristics of diode devices. A knowledge-based neural network model for packaged diodes is proposed, which enhances modeling accuracy by learning the discrepancies between the diode coarse model and the actual device data. A training method for rapid parameter adjustment is suggested, where the neural networks within the input and output packaging modules automatically learn and adjust, continuously optimizing their internal parameters to enhance modeling efficiency. Modeling experiments conducted on the measurement data of the MA4AGFCP910 diode show that the proposed packaged diode model can effectively and accurately match the small-signal characteristic data of the diode device.
Citation
Wenyuan Liu, Ningning Yang, Shuxia Yan, and Yanlin Xu, "A Novel Knowledge-Based Neural Network Approach to the Small-Signal Modeling of Packaged Diodes," Progress In Electromagnetics Research C, Vol. 149, 9-14, 2024.
doi:10.2528/PIERC24080901
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