1 citations · 1 across the 4 of their papers we have counts for
4 papers
Inverse Design of Compact and Wideband Inverted Doherty Power Amplifiers Using Deep Learning
Han Zhou, Haojie Chang, David Widen +1
This paper presents a deep learning-assisted methodology for the inverse synthesis of a compact, wideband inverted Doherty power amplifier (PA). Convolutional neural networks (CNNs…
Deep-Learning-Based Pixelated Microwave Filter Design and Characterization using Electro-Optical Electric-Field Measurements
Han Zhou, Richard Bannister, Caspar Pierce +9
Traditional microwave filter design typically relies on iterative parameter tuning and predefined topologies, which limits design space and increases development time. This study u…
Deep Learning-Driven Inverse Design of Doherty Power Amplifiers Using Pixelated Combiners and Dual-State Impedance Synthesis
Han Zhou, Haojie Chang, David Widen +1
The output combiner of a Doherty power amplifier (PA) integrates load modulation, impedance matching, and phase compensation within a single network, making its design and synthesi…
Run-Time Accuracy Reconfigurable Stochastic Computing for Dynamic Reliability and Power Management
Shuyuan Yu, Han Zhou, Shaoyi Peng +3
In this paper, we propose a novel accuracy-reconfigurable stochastic computing (ARSC) framework for dynamic reliability and power management. Different than the existing stochastic…