Signal processing is of critical importance for various science and technology fields. Analog optical processing can provide an effective solution to perform large-scale and real-time data processing, superior to its digital counterparts, which have the disadvantages of low operation speed and large energy consumption. As an important branch of modern optics, Fourier optics exhibits great potential for analog optical image processing, for instance for edge detection. While these operations have been commonly explored to manipulate the spatial content of an image, mathematical operations that act directly over the angular spectrum of an image have not been pursued. Here, we demonstrate manipulation of the angular spectrum of an image, and in particular its differentiation, using dielectric metasurfaces operating across the whole visible spectrum. We experimentally show that this technique can be used to enhance desired portions of the angular spectrum of an image. Our approach can be extended to develop more general angular spectrum analog meta-processors, and may open opportunities for optical analog data processing and biological imaging. 

Broadband angular spectrum differentiation using dielectric metasurfaces

Machine vision systems that capture images for visual inspection and recognition tasks must be able to perceive, memorize, and compute any color scene. To achieve this, most of the current visual systems use circuits and algorithms which may reduce efficiency and increase complexity. Herein, a 2D semiconductor tungsten diselenide (WSe2)-based phototransistor that successfully demonstrates an artificial vision system integrating the processing capability of visual information sensing memory, is reported. Furthermore, based on a 6 × 6 fabricated retinal perception array, artificial visual information sensing memory and processing system are proposed to perform image recognition tasks, which can avoid the time delay and energy consumption caused by data conversion and movement. On the other hand, highly linear symmetric synaptic plasticity can be achieved based on the modulation of carrier types in WSe2 transistors with different thicknesses, facilitating the high level of training and inference accuracy for artificial neural networks. Last, through training and inference simulations, the feasibility of the hybrid synapses for optical neural networks (ONN) is demonstrated. 

Bioinspired Artificial Visual System Based on 2D WSe2 Synapse Array

Parallel in-memory wireless computing

Optoelectronic memory in 2D MoS2 field effect transistor

Machine‐learning‐enhanced nanosensors are rapidly emerging as a promising solution in the field of sensor technology, as traditional sensors encounter limitations of data analysis in their development. Since the inception of machine‐learning algorithms being applied to enhance nanosensors, they have gained significant attention due to their adaptive and predictive capabilities, which promise to dramatically improve efficiency in data collection and processing applications. Herein, a comprehensive overview of technological innovation is provided by reviewing the latest developments in cloud computing, edge computing, and the burgeoning realm of neuromorphic computing. Cloud computing has emerged as a powerhouse, harnessing formidable computational capabilities to process vast volumes of high‐dimensional data. Then, the research directions for various applications of these cloud artificial intelligence (AI)‐enhanced nanosensors are outlined. Moreover, the integration of AI and nanosensor technology into chip‐level edge computing, although promising, still faces challenges such as energy‐efficient hardware development, algorithm optimization, and scalability for mass production. Finally, a forward‐looking perspective on the future of machine‐learning‐enhanced nanosensors is provided, delineating the challenges and opportunities for further research and innovation in this exciting field.

Advances in Machine‐Learning Enhanced Nanosensors: From Cloud Artificial Intelligence Toward Future Edge Computing at Chip Level

In-sensor dynamic computing for intelligent machine vision

Over the last few decades, advance in material science has made it possible to control the dimensions of materials structures with atomic scale precision and to realize various low‐dimensional heterostructures. These heterostructures have been widely used in the electronic and spintronic as well as optoelectronic devices, with great success. Engineering the dimensionality and physical properties of low‐dimensional materials enables to design a large variety of low‐dimensional artificial heterostructures. The inherent physical properties that these emerging low‐dimensional heterostructures exhibit open unprecedented opportunities for neuromorphic computing applications, e.g., neuromorphic electronics, neuromorphic spintronics, and neuromorphic optoelectronics, which lay the foundation for building up intelligent system in the future. Herein, the current main status of the emerging low‐dimensional heterostructures used in neuromorphic electronic, neuromorphic spintronic, and neuromorphic optoelectronic devices is reviewed, and the relevant challenges and outlook for future advances are discussed.

Emerging Low‐Dimensional Heterostructure Devices for Neuromorphic Computing

2D materials for intelligent devices

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In-sensor dynamic computing for intelligent machine vision

Parallel in-memory wireless computing

Emerging Low‐Dimensional Heterostructure Devices for Neuromorphic Computing

2D materials for intelligent devices