Complex-valued neural networks have many advantages over their real-valued counterparts. Conventional digital electronic computing platforms are incapable of executing truly complex-valued representations and operations. In contrast, optical computing platforms that encode information in both phase and magnitude can execute complex arithmetic by optical interference, offering significantly enhanced computational speed and energy efficiency. However, to date, most demonstrations of optical neural networks still only utilize conventional real-valued frameworks that are designed for digital computers, forfeiting many of the advantages of optical computing such as efficient complex-valued operations. In this article, we highlight an optical neural chip (ONC) that implements truly complex-valued neural networks. We benchmark the performance of our complex-valued ONC in four settings: simple Boolean tasks, species classification of an Iris dataset, classifying nonlinear datasets (Circle and Spiral), and handwriting recognition. Strong learning capabilities (i.e., high accuracy, fast convergence and the capability to construct nonlinear decision boundaries) are achieved by our complex-valued ONC compared to its real-valued counterpart.

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Silicon photonic integrated circuits for telecommunication and data centers have been well studied in the past decade, and now most related efforts have been progressing toward commercialization. Scaling up the silicon-oninsulator (SOI)-based device dimensions in order to extend the operation wavelength to the short mid-infrared (MIR) range (2-4 μm) is attracting research interest, owing to the host of potential applications in lab-on-chip sensors, free space communications, and much more. Other material systems and technology platforms, including silicon-on-silicon nitride, germanium-on-silicon, germanium-on-SOI, germanium-on-silicon nitride, sapphireon-silicon, SiGe alloy-on-silicon, and aluminum nitride-on-insulator are explored as well in order to realize low-loss waveguide devices for different MIR wavelengths. In this paper, we will comprehensively review silicon photonics for MIR applications, with regard to the state-of-the-art achievements from various device demonstrations in different material platforms by various groups. We will then introduce in detail of our institute's research and development efforts on the MIR photonic platforms as one case study. Meanwhile, we will discuss the integration schemes along with remaining challenges in devices (e.g., light source) and integration. A few application-oriented examples will be examined to illustrate the issues needing a critical solution toward the final production path (e.g., gas sensors). Finally, we will provide our assessment of the outlook of potential future research topics and engineering challenges along with opportunities.

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