Photodetectors hold a critical position in optoelectronic integrated circuits, and they convert light into electricity. Over the past decades, high‐performance photodetectors (PDs) have been aggressively pursued to enable high‐speed, large‐bandwidth, and low‐noise communication applications. Various material systems have been explored and different structures designed to improve photodetection capability as well as compatibility with CMOS circuits. In this paper, we review state‐of‐theart photodetection technologies in the telecommunications spectrum based on different material systems, including traditional semiconductors such as InGaAs, Si, Ge and HgCdTe, as well as recently developed systems such as low‐dimensional materials (e.g. graphene, carbon nanotube, etc.) and noble metal plasmons. The corresponding material properties, fundamental mechanisms, fabrication, theoretical modelling and performance of the typical PDs are presented, including the emerging directions and perspectives of the PDs for optoelectronic integration applications are discussed.

Integrated photodetectors are essential components of scalable photonics platforms for quantum and classical applications. However, most efforts in the development of such devices to date have been focused on infrared telecommunications wavelengths. Here, we report the first monolithically integrated avalanche photodetector (APD) for visible light. Our devices are based on a doped silicon rib waveguide with a novel end-fire input coupling to a silicon nitride waveguide. We demonstrate a high gain-bandwidth product of 234 ± 25 GHz at 20 V reverse bias measured for 685 nm input light, with a low dark current of 0.12 μA. We also observe open eye diagrams at up to 56 Gbps. This performance is very competitive when benchmarked against other integrated APDs operating in the infrared range. With CMOS-compatible fabrication and integrability with silicon photonic platforms, our devices are attractive for sensing, imaging, communications, and quantum applications at visible wavelengths.

With recent rapid advances in photonic integrated circuits, it has been demonstrated that programmable photonic chips can be used to implement artificial neural networks. Convolutional neural networks (CNN) are a class of deep learning methods that have been highly successful in applications such as image classification and speech processing. We present an architecture to implement a photonic CNN using the Fourier transform property of integrated star couplers. We show, in computer simulation, high accuracy image classification using the MNIST dataset. We also model component imperfections in photonic CNN and show that the performance degradation can be recovered in a programmable chip. Our proposed architecture provides a large reduction in physical footprint compared to current implementations as it utilizes the natural advantages of optics and hence offers a scalable pathway towards integrated photonic deep learning processors.

An efficient method to extract equivalent radiated emission sources for printed circuit boards (PCBs) from near-field (NF) scanning data is presented. The equivalent sources are represented by elemental magnetic dipoles placed over a planar surface on the component side of a PCB. A global optimization procedure, based on the differential evolution algorithm, is used to determine the components of each dipole moment, by exploiting only the magnitude of the scanned NF data. The number and positions of the equivalent dipoles are predetermined by analyzing the scanning NF data, which is based on the NF radiation pattern of a single straight trace on a PCB. The effects of both of the spacing of the scanning points and the distance between the scanning and the reconstruction planes on the accuracy of the reconstructed results are studied. The proposed method is validated by the comparison of the reconstructed and full-wave results for PCBs with different types of traces.

A near-field scanning based equivalent dipole model is introduced to simplify the analysis of the emission from an electronic component at its nearby receivers. The model consists of a set of infinitesimal magnetic dipoles, with the number, position, and moment of the dipoles being unknowns. A nonlinear optimization problem in terms of the unknown parameters is established through minimizing the difference between the magnitude of magnetic fields obtained by near-field scanning and the radiation from the equivalent dipoles. The problem is difficult to solve as the number of dipoles is unknown. A differential evolution method based global optimization algorithm is proposed to solve the problem. A set of subproblems is first derived. It is then solved sequentially one after the other by using the proposed algorithm. A combination of the obtained solutions forms a solution of the original optimization problem. Numerical results are presented to demonstrate the validity and efficiency of the proposed algorithm.