Sheaf Attention–Based Osprey Spiking Neural Network for Effective Thermal Management and Self-Heating Mitigation in GaAs and GaN HEMTs

Abstract

This research introduces the sheaf attention–based osprey spiking neural network (SA-OSNN) to optimize the thermal performance of GaAs and GaN high electron mobility transistors (HEMTs), which are critical for radio frequency and microwave circuits due to their excellent electron characteristics. By integrating modified osprey optimization, the SA-OSNN approach enhances thermal management by dynamically adjusting model parameters in response to changing environmental conditions, ensuring efficient and effective thermal control. This method is used as an optimization tool that works in conjunction with established thermal management solutions, such as GaN, SiC, and AlN materials, which provide the physical properties necessary for effective heat dissipation. This analysis covers a temperature between −100°C and 200°C, examining frequencies up to 50 GHz validating the accuracy and reliability for GaAs and GaN HEMT thermal optimization. Overall, this research achieves a minimum error of 5.97834e−01 and 6.01251e−05. Also, SA-OSNN achieves an accuracy of 97% with better performances than existing methods.