We demonstrated all-optical demultiplexing of 160-Gb/s signal to 40- and 80-Gb/s by a Mach-Zehnder Interferometric all-optical switch, where the picosecond cross-phase modulation (XPM) induced by intersubband excitation in InGaAs/AlAsSb coupled double quantum wells is utilized. A bi-directional pump configuration, i.e., two control pulses are injected from both sides of a waveguide chip simultaneously, increases a nonlinear phase shift twice in comparison with injection of single pump beam with forward- and backward direction. The bi-directional pump configuration is the effective way to avoid damaging waveguide facets in the case where high optical power of control pulse is necessary to be injected for optical gating at repetition rate of 40/80 GHz. Bit error rate (BER) measurements on 40-Gb/s demultiplexed signal show that the power penalty is decreased slightly for the bi-directional pump case in the BER range less than 10-6. The power penalty is 1.3 dB at BER of 10 - 9 for the bi-directional pump case, while it increases by 0.3-0.6 dB for single pump cases. A power penalty is influenced mainly by signal attenuation at "off" state due to the insufficient nonlinear phase shift, upper limit of which is constrained by the current low XPM efficiency of 0.1 rad/pJ and the damage threshold power of 100 mW in a waveguide facet.

Wide deployment of artificial intelligence (AI) is inducing exponentially growing energy consumption. Traditional digital platforms are becoming difficult to fulfill such ever-growing demands on energy efficiency as well as computing latency, which necessitates the development of high efficiency analog hardware platforms for AI. Recently, optical and electrooptic hybrid computing is reactivated as a promising analog hardware alternative because it can accelerate the information processing in an energy-efficient way. Integrated photonic circuits offer such an analog hardware solution for implementing photonic AI and machine learning. For this purpose, we proposed a photonic analog of support vector machine and experimentally demonstrated low-latency and low-energy classification computing, which evidences the latency and energy advantages of optical analog computing over traditional digital computing. We also proposed an electrooptic Hopfield network for classifying and recognizing time-series data. This paper will review our work on implementing classification computing and Hopfield network by leveraging silicon photonic circuits.