300-Gbps optical interconnection using neural-network based silicon microring modulator
Fangchen Hu, Yuguang Zhang, Hongguang Zhang, Zhongya Li, Sizhe Xing, Jianyang Shi, Junwen Zhang, Xi Xiao, Nan Chi, Zhixue He, Shaohua Yu
Abstract
Abstract Silicon microring modulators (Si-MRM) are critical components for high-performance electro-optical (E-O) signal conversion at optical interconnections due to their ultrawide bandwidth. However, the current transmission speed at the interconnections is still limited to 240 Gbps because of the low spectral-efficiency, as a result of the inherent modulation nonlinearity of Si-MRMs. Here, we theoretically analyse the modulation nonlinearity of a depletion-mode Si-MRM. Based on the analytical results, we further propose a physics-inspired neural network, named as bidirectional gate recurrent unit (Bi-GRU) to mitigate the signal distortion in Si-MRMs. Bi-GRU matches the analytical E-O modulation dynamics within Si-MRMs, thus can accurately capture the impairment features and accelerate the data transmission speed. We then fabricate a Si-MRM with −3dB E-O bandwidth of 42.5 GHz, achieving an ultrahigh speed optical interconnection with a data rate of 302 Gbps. The maximum spectral-efficiency of modulated signals is improved to 5.20 bit/s/Hz. The results provide insights to develop ultrahigh-speed Si-MRM using emerging AI techniques.