This article presents a deep learning-driven inverse design methodology for Doherty power amplifiers (PA) with multi-port pixelated output combiner networks. A deep convolutional neural network (CNN) is developed and trained as an electromagnetic (EM) surrogate model to accurately and rapidly predict the S-parameters of pixelated passive networks. By leveraging the CNN-based surrogate model within a blackbox Doherty framework and a genetic algorithm (GA)-based optimizer, we effectively synthesize complex Doherty combiners that enable an extended back-off efficiency range using fully symmetrical devices. As a proof of concept, we designed and fabricated two Doherty PA prototypes incorporating three-port pixelated combiners, implemented with GaN HEMT transistors. In measurements, both prototypes demonstrate a maximum drain efficiency exceeding 74% and deliver an output power surpassing 44.1 dBm at 2.75 GHz. Furthermore, a measured drain efficiency above 52% is maintained at the 9-dB back-off power level for both prototypes at the same frequency. To evaluate linearity and efficiency under realistic signal conditions, both prototypes are tested using a 20-MHz 5G new radio (NR)-like waveform exhibiting a peak-to-average power ratio (PAPR) of 9.0 dB. After applying digital predistortion (DPD), each design achieves an average power added efficiency (PAE) above 51%, while maintaining an adjacent channel leakage ratio (ACLR) better than -60.8 dBc.

翻译：本文提出了一种基于深度学习的逆向设计方法，用于具有多端口像素化输出合成器网络的多赫蒂功率放大器。我们开发并训练了一个深度卷积神经网络作为电磁代理模型，以准确快速地预测像素化无源网络的S参数。通过将基于CNN的代理模型嵌入黑盒多赫蒂框架，并采用基于遗传算法的优化器，我们有效合成了复杂的多赫蒂合成器，该合成器利用完全对称器件实现了扩展的回退效率范围。作为概念验证，我们设计并制造了两个采用三端口像素化合成器的多赫蒂功率放大器原型，均使用GaN HEMT晶体管实现。测量结果表明，两个原型在2.75 GHz频率下均实现了超过74%的最大漏极效率，并提供了高于44.1 dBm的输出功率。此外，在相同频率下，两个原型在9 dB回退功率电平处均保持了高于52%的实测漏极效率。为评估实际信号条件下的线性度与效率，两个原型均采用峰值平均功率比为9.0 dB的20 MHz 5G新无线电类波形进行测试。在应用数字预失真后，每个设计均实现了高于51%的平均功率附加效率，同时保持优于-60.8 dBc的邻道泄漏比。