徐绍夫的工作——Deep-learning-powered photonic ADC的相关研究成果近期发表在Light:Science and Applications期刊上，该工作得到了国家自然科学基金的部分资助 (grant no. 61822508, 61571292 and 61535006)。
摘要：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 d emonstrate 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.