用深度学习设计紧凑宽频逆Doherty功放，集成多重功能于一身。
Inverse Design of Compact and Wideband Inverted Doherty Power Amplifiers Using Deep Learning
- 结合CNN与遗传算法，生成集成多种功能的像素化功放组合器。
- 1.9-2.5 GHz内峰值效率51%-63%，6dB回退效率48%-54%。
- 适合射频电路设计、高效功放研发人员参考。
本文提出一种基于深度学习的逆向设计方法，用于实现紧凑型、宽频带逆Doherty功率放大器（PA）。通过卷积神经网络（CNN）与遗传算法（GA）协同优化，生成像素化的Doherty组合器结构，将负载调制、阻抗匹配、功率合成和相位补偿集成于一体。以氮化镓高电子迁移率晶体管（GaN HEMT）为例，设计并制作了原型机。实测结果表明，在1.9-2.5 GHz频段内，峰值漏极效率为51%-63%，6 dB回退效率为48%-54%，输出功率为44±0.3 dBm。在应用数字预失真（DPD）后，邻道泄漏比（ACLR）优于-53.2 dBc。
原文摘要 · Abstract (English)
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) and genetic algorithms (GAs) are jointly employed to generate pixelated Doherty combiner networks that integrate load modulation, impedance matching, power combining, and phase compensation into a single structure. As a proof of concept, we design and fabricate a GaN HEMT Doherty PA with a pixelated output combiner. The prototype achieves a measured peak drain efficiency of 51%-63% and a 6-dB back-off efficiency of 48%-54% over 1.9-2.5 GHz. Within the same frequency range, the measured output power is 44+/-0.3 dBm. Furthermore, with digital predistortion (DPD) applied, the prototype circuit demonstrates an adjacent channel leakage ratio (ACLR) better than -53.2 dBc.
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