By constructing multiple layers of neurons and applying appropriate training methods, data from images, audio, and video can be automatically extracted with representations to be used in the inference of unknown data. Recently, deep learning technologies 10 have made substantial advances in a variety of artificial intelligence applications, such as computer vision 11, 12, medical diagnosis 13, and gaming 14. However, since the imperfect properties and setups of photonic components give rise to system defects and can deteriorate the performance of ADCs 4, 8, 9, designing an advanced ADC architecture remains challenging. Facilitated by photonic technologies, the bottlenecks of bandwidth limitations and timing jitter are elegantly overcome 4. Traditionally, in modern information systems, electronic analog-to-digital conversion methods have supported high-accuracy quantization and operational stability due to the mature manufacturing of electronic components nevertheless, their bandwidth limitations and high timing jitter hinder the development of electronic methods toward broadband high-accuracy ADCs for next-generation information systems 3, 4, 5, 6, 7. Next-generation information systems, such as radar, imaging, and communications systems, are aimed at realizing high operation frequencies and broad bandwidths, and require analog-to-digital converters (ADCs) with high sampling rate, broadband coverage, and sufficient accuracy 1, 2, 3. 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. 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. 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. 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. 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.
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