As a successful case of combining deep learning with photonics, the research on optical machine learning has recently undergone rapid development. Among various optical classification frameworks, diffractive networks have been shown to have unique advantages in all-optical reasoning. As an important property of light, the orbital angular momentum (OAM) of light shows orthogonality and mode-infinity, which can enhance the ability of parallel classification in information processing. However, there have been few all-optical diffractive networks under the OAM mode encoding. Here, we report a strategy of OAM-encoded diffractive deep neural network (OAM-encoded D²NN) that encodes the spatial information of objects into the OAM spectrum of the diffracted light to perform all-optical object classification. We demonstrated three different OAM-encoded D²NNs to realize (1) single detector OAM-encoded D²NN for single task classification, (2) single detector OAM-encoded D²NN for multitask classification, and (3) multidetector OAM-encoded D²NN for repeatable multitask classification. We provide a feasible way to improve the performance of all-optical object classification and open up promising research directions for D²NN by proposing OAM-encoded D²NN.

Topological photonic states have promising applications in slow light, photon sorting, and optical buffering. However, realizing such states in non-Hermitian systems has been challenging due to their complexity and elusive properties. In this work, we have experimentally realized a topological rainbow in non-Hermitian photonic crystals by controlling loss in the microwave frequency range for what we believe is the first time. We reveal that the lossy photonic crystal provides a reliable platform for the study of non-Hermitian photonics, and loss is also taken as a degree of freedom to modulate topological states, both theoretically and experimentally. This work opens a way for the construction of a non-Hermitian photonic crystal platform, will greatly promote the development of topological photonic devices, and will lay a foundation for the real-world applications.

Optical neural networks (ONNs), enabling low latency and high parallel data processing without electromagnetic interference, have become a viable player for fast and energy-efficient processing and calculation to meet the increasing demand for hash rate. Photonic memories employing nonvolatile phase-change materials could achieve zero static power consumption, low thermal cross talk, large-scale, and high-energy-efficient photonic neural networks. Nevertheless, the switching speed and dynamic energy consumption of phase-change material-based photonic memories make them inapplicable for in situ training. Here, by integrating a patch of phase change thin film with a PIN-diode-embedded microring resonator, a bifunctional photonic memory enabling both 5-bit storage and nanoseconds volatile modulation was demonstrated. For the first time, a concept is presented for electrically programmable phase-change material-driven photonic memory integrated with nanosecond modulation to allow fast in situ training and zero static power consumption data processing in ONNs. ONNs with an optical convolution kernel constructed by our photonic memory theoretically achieved an accuracy of predictions higher than 95% when tested by the MNIST handwritten digit database. This provides a feasible solution to constructing large-scale nonvolatile ONNs with high-speed in situ training capability.

The basic indexes of all-optical integrated photonic circuits include high-density integration, ultrafast response and ultra-low energy consumption. Traditional methods mainly adopt conventional micro/nano-structures. The overall size of the circuit is large, usually reaches hundreds of microns. Besides, it is difficult to balance the ultrafast response and ultra-low energy consumption problem, and the crosstalk between two traditional devices is difficult to overcome. Here, we propose and experimentally demonstrate an approach based on inverse design method to realize a high-density, ultrafast and ultra-low energy consumption integrated photonic circuit with two all-optical switches controlling the input states of an all-optical XOR logic gate. The feature size of the whole circuit is only 2.5 μm × 7 μm, and that of a single device is 2 μm × 2 μm. The distance between two adjacent devices is as small as 1.5 μm, within wavelength magnitude scale. Theoretical response time of the circuit is 150 fs, and the threshold energy is within 10 fJ/bit. We have also considered the crosstalk problem. The circuit also realizes a function of identifying two-digit logic signal results. Our work provides a new idea for the design of ultrafast, ultra-low energy consumption all-optical devices and the implementation of high-density photonic integrated circuits.The basic indexes of all-optical integrated photonic circuits include high-density integration, ultrafast response and ultra-low energy consumption. Traditional methods mainly adopt conventional micro/nano-structures. The overall size of the circuit is large, usually reaches hundreds of microns. Besides, it is difficult to balance the ultrafast response and ultra-low energy consumption problem, and the crosstalk between two traditional devices is difficult to overcome. Here, we propose and experimentally demonstrate an approach based on inverse design method to realize a high-density, ultrafast and ultra-low energy consumption integrated photonic circuit with two all-optical switches controlling the input states of an all-optical XOR logic gate. The feature size of the whole circuit is only 2.5 μm × 7 μm, and that of a single device is 2 μm × 2 μm. The distance between two adjacent devices is as small as 1.5 μm, within wavelength magnitude scale. Theoretical response time of the circuit is 150 fs, and the threshold energy is within 10 fJ/bit. We have also considered the crosstalk problem. The circuit also realizes a function of identifying two-digit logic signal results. Our work provides a new idea for the design of ultrafast, ultra-low energy consumption all-optical devices and the implementation of high-density photonic integrated circuits.

The quantum Toffoli gate is one of the most important three-qubit gates, but it is challenging to construct a chip according to the complicated traditional circuit. Using the optimized 3D configuration with an overpass waveguide to reduce the circuit complexity, we successfully fabricate an on-chip path encoded photonic quantum Toffoli gate enabled by the 3D capability of the femtosecond laser direct writing (FLDW) for the first time to our knowledge, whose truth-table fidelity is higher than 85.5%. Furthermore, a path encoded four-qubit controlled-controlled-controlled NOT gate is written to confirm the scalability of this resource-saving technique. This work paves the way for the FLDW of more complex and powerful photonic quantum computation chips.

The rapid development of information technology has fueled an ever-increasing demand for ultrafast and ultralow-energy-consumption computing. Existing computing instruments are pre-dominantly electronic processors, which use electrons as information carriers and possess von Neumann architecture featured by physical separation of storage and processing. The scaling of computing speed is limited not only by data transfer between memory and processing units, but also by RC delay associated with integrated circuits. Moreover, excessive heating due to Ohmic losses is becoming a severe bottleneck for both speed and power consumption scaling. Using photons as information carriers is a promising alternative. Owing to the weak third-order optical nonlinearity of conventional materials, building integrated photonic computing chips under traditional von Neumann architecture has been a challenge. Here, we report a new all-optical computing framework to realize ultrafast and ultralow-energy-consumption all-optical computing based on convolutional neural networks. The device is constructed from cascaded silicon Y-shaped waveguides with side-coupled silicon waveguide segments which we termed “weight modulators” to enable complete phase and amplitude control in each waveguide branch. The generic device concept can be used for equation solving, multifunctional logic operations as well as many other mathematical operations. Multiple computing functions including transcendental equation solvers, multifarious logic gate operators, and half-adders were experimentally demonstrated to validate the all-optical computing performances. The time-of-flight of light through the network structure corresponds to an ultrafast computing time of the order of several picoseconds with an ultralow energy consumption of dozens of femtojoules per bit. Our approach can be further expanded to fulfill other complex computing tasks based on non-von Neumann architectures and thus paves a new way for on-chip all-optical computing.

随着超快光学的发展和对以Bi₂Te₃为代表的拓扑绝缘体材料研究的深入，近几年，将拓扑绝缘体薄膜应用于超快光器件的研究方向发展迅速并发表了一系列研究成果，本文综述了近年来基于拓扑绝缘体材料的超快激光及光器件的研究。从材料结构及制备方法出发，介绍了其独特的光学及光电特性，总结了其在超快激光及光器件中的应用研究进展，回顾和讨论了这一领域的成就和挑战，并对将拓扑绝缘体薄膜材料应用于超快光器件的进一步研究进行了展望。