Analog-to-digital converters (ADCs) must be high speed, broadband, and accurate for the development of modern information systems, such as radar, imaging, and communications systems; photonic technologies are regarded as promising technologies for realizing these advanced requirements. Here, we present a deep-learning-powered photonic ADC architecture that simultaneously exploits the advantages of electronics and photonics and overcomes the bottlenecks of the two technologies, thereby overcoming the ADC tradeoff among speed, bandwidth, and accuracy. Via supervised training, the adopted deep neural networks learn the patterns of photonic system defects and recover the distorted data, thereby maintaining the high quality of the electronic quantized data succinctly and adaptively. The numerical and experimental results demonstrate that the proposed architecture outperforms state-of-the-art ADCs with developable high throughput; hence, deep learning performs well in photonic ADC systems. We anticipate that the proposed architecture will inspire future high-performance photonic ADC design and provide opportunities for substantial performance enhancement for the next-generation information systems.

模数转换器（ADC）必须是高速，宽带且准确的，才能开发现代信息系统，例如雷达，成像和通信系统；光子技术被认为是实现这些高级要求的有前途的技术。在这里，我们介绍了一种具有深度学习能力的光子ADC架构，该架构同时利用电子学和光子学的优势，克服了这两种技术的瓶颈，从而克服了ADC在速度，带宽和精度之间的权衡。通过监督培训，采用的深度神经网络学习了光子系统缺陷的模式并恢复了失真的数据，从而简洁，自适应地保持了电子量化数据的高质量。数值和实验结果表明，所提出的架构具有可开发的高吞吐量，性能优于最新的ADC。因此，深度学习在光子ADC系统中表现良好。我们预计拟议的架构将激发未来的高性能光子ADC设计，并为大幅提高下一代信息系统的性能提供机会。