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		题名	作者	年代	出处	被引量
	1	Electrically programmable phase-change photonic memory for optical neural networks with nanoseconds in situ training capability显示文摘Optical neural networks (ONNs), enabling low latency and high parallel data processing withoutelectromagnetic interference, have become a viable player for fast and energy-efficient processing andcalculation to meet the increasing demand for hash rate. Photonic memories employing nonvolatile phase-change materials could achieve zero static power consumption, low thermal cross talk, large-scale, andhigh-energy-efficient photonic neural networks. Nevertheless, the switching speed and dynamic energyconsumption of phase-change material-based photonic memories make them inapplicable for in situ training.Here, by integrating a patch of phase change thin film with a PIN-diode-embedded microring resonator,a bifunctional photonic memory enabling both 5-bit storage and nanoseconds volatile modulation wasdemonstrated. For the first time, a concept is presented for electrically programmable phase-changematerial-driven photonic memory integrated with nanosecond modulation to allow fast in situ training and zerostatic power consumption data processing in ONNs. ONNs with an optical convolution kernel constructedby our photonic memory theoretically achieved an accuracy of predictions higher than 95% when testedby the MNIST handwritten digit database. This provides a feasible solution to constructing large-scalenonvolatile ONNs with high-speed in situ training capability.	Maoliang Wei Junying Li Zequn Chen Bo Tang Zhiqi Jia Peng Zhang Kunhao Lei Kai Xu Jianghong Wu Chuyu Zhong Hui Ma Yuting Ye Jialing Jian Chunlei Sun Ruonan Liu Ying Sun Wei.E.I.Sha Xiaoyong Hu Jianyi Yang Lan Li Hongtao Lin	2023	Advanced Photonics2023，5，4：	0

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