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Towards Chip-Scale Photonic Computing Architecture, Devices and Materials

There is a race between massively growing amounts of data and the capacity of computing units. Photonic computing—extending to optical neural networks (ONNs), optical AI, and machine learning—provides an appealing solution due to the inherent parallelism in optical signal processing through wavelength de/multiplexing. Photonic computing has rapid signal-processing speed and consumes no or very little power in signal routing. In recent years, with the establishment of Si, SiN, and InP photonic foundries, photonic systems scaling on high-yield commercial platforms has become a reality. This Special Topic highlights recent advancement in chip-scale photonic computing systems, photonic computing architecture, photonic AI accelerators, photonic activation function, and novel devices and materials for optical signal processing.

Guest Editors: Rena Huang and Ajey Jacob

Meng Zhang; Dennis Yin; Nicholas Gangi; Amir Begović; Alexander Chen; Zhaoran Rena Huang; Jiaqi Gu
C. Gautam; M. Pan; Y. Chen; T. J. Rotter; G. Balakrishnan; W. Zhou
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