Hybrid quantum-classical neural network reduces overall normalized RMSE by 24.4% versus classical ANN when predicting six electrical targets for AlGaN/GaN MIS-HEMTs on 468 experimental devices from 17 process splits.
Table-Based Nonlinear HEMT Model Extracted from Time-Domain Large-Signal Measurements
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Hybrid Classical-Quantum Neural Networks for Multi-Characteristic Co-Optimization of Recessed-Gate AlGaN/GaN MIS-HEMTs
Hybrid quantum-classical neural network reduces overall normalized RMSE by 24.4% versus classical ANN when predicting six electrical targets for AlGaN/GaN MIS-HEMTs on 468 experimental devices from 17 process splits.