Zian Xiao

Photonic sensors play an increasingly important role in chemical analysis, but conventional systems rely on sequential wavelength scanning and extensive electronic post-processing, leading to large data redundancy, high latency, and excessive energy consumption. Here, we introduce an end-to-end all-optical in-sensor computing system based on photonic integrated circuits that merges sensing and computing in the optical domain. The system integrates a photonic waveguide sensor and a microring weight bank on a single chip for linear processing, while nonlinear activation is implemented using an erbium-doped fiber amplifier. By performing spectral compression, this system enables real-time optical-domain computing with up to 6-bit precision. The system achieves a classification accuracy of 94.2% across 27 liquid-mixture classes and concentration prediction for mixture chemicals. Compared with conventional photonic sensing systems, the proposed architecture reduces inference latency by a factor of 4.48 and energy consumption by a factor of 11.47. These results establish a compact, low-redundancy, and energy-efficient paradigm for intelligent photonic sensing at the edge.

In recent years, with the further ministration of the semiconductor device in integrated circuits, power consumption and data transmission bandwidth have become insurmountable obstacles. As an integrated technology, photonic integrated circuits (PICs) have a promising potential in the post‐Moore era with more advantages in data processing, communication, and diversified sensing applications for their ultra‐high process speed and low power consumption. Silicon photonics is believed to be an encouraging solution to realize PICs because of the mature CMOS process. The past decades have witnessed a huge growth in silicon PICs. However, there is still a demand for the development of silicon PICs to enable powerful chip‐scale systems and new functionalities. In this paper, a review of the photonic components, functional blocks, and emerging applications for PICs is offered. The common photonic components are classified into several sections, including on‐chip light sources, fiber‐to‐chip couplers, photonic resonators, waveguide‐based sensors, on‐chip photodetectors, and modulators. The functional blocks of the PICs mentioned in this review are photonic memories and photonic neural networks. Finally, the paper concludes with emerging applications for further study.

Security is a prevailing concern in communication as conventional encryption methods are challenged by progressively powerful supercomputers. Here, we show that biometrics-protected optical communication can be constructed by synergizing triboelectric and nanophotonic technology. The synergy enables the loading of biometric information into the optical domain and the multiplexing of digital and biometric information at zero power consumption. The multiplexing process seals digital signals with a biometric envelope to avoid disrupting the original high-speed digital information and enhance the complexity of transmitted information. The system can perform demultiplexing, recover high-speed digital information, and implement deep learning to identify 15 users with around 95% accuracy, irrespective of biometric information data types (electrical, optical, or demultiplexed optical). Secure communication between users and the cloud is established after user identification for document exchange and smart home control. Through integrating triboelectric and photonics technology, our system provides a low-cost, easy-to-access, and ubiquitous solution for secure communication.