Smart textile for sensor is identified as a superior platform with greatly improved convenience and comfort for wearable bioelectronics. However, most reported textile-based sensors cannot fully demonstrate the inherent advantages of textiles, such as comfortability, breathability, biocompatibility, and environmental friendliness, mainly due to the intrinsic limitation of non-textile or inorganic components. Here, an all-textile, all-organic, washable, and breathable sensor with discriminable pressure, proximity, and temperature sensing function is first reported. Multiple sensing functions and outstanding washability are demonstrated. The all-textile sensor can also be seamlessly integrated into diverse types of fabrics to realize wide-range sensing of human activities and noncontact stimuli without sacrificing biocompatibility and comfortability. Additionally, by combining with the deep-learning technique, an all-textile sensing system is established to recognize object shape, contactless trajectory, and even environmental temperature. These results open a new avenue for designing low-cost, washable, comfortable, and biocompatible green textile electronics, providing a meaningful guideline in intelligent textiles.

Neumann system is a conventional computing architecture with divided processor and memory unit which executes computational tasks sequentially which has served as a pillar of contemporary computing since 1945. However, the frequent data shuffling between the separated processor and memory unit induce massive power consumption and latency which is so-called von Neumann bottleneck. [7,8] The human brain is capable of concurrently executing several complicated tasks with enormous parallelism with extremely low power consumption, outstanding fault tolerance, and remarkable durability owing to its extensive connection, functional organizational hierarchy, advanced learning rules, and neuronal plasticity. Inspiring from it, Mead first pioneered the notion of "neuromorphic computing" in the late 1980s and early 1990s. [9,10] Accordingly, to eliminate inherent constraint of the von Neumann systems, substantial effort has been focused on investigating neuromorphic computing systems. [11][12][13] Biological neuromorphic systems consisting 10 11 neurons interconnected with each other via 10 15 synapses [14,15] is capable to respond to environment and history at different levels from simple molecular (nucleic acids could displays adaptive behaviors including self-repair and replication, under stimuli from the local environment), the elementary information-processing blocks in biological systems (neurons could exhibit more than 20 different dynamic behaviors triggered by historically and environmentally electrochemical stimulation), to whole functional systems with more hierarchical complexity (extremely low or high relative humidity (RH), has a substantial impact on the accuracy of human visual system). [16][17][18] Recently, inspired by human sensory processing and perceptual learning, neuromorphic sensing and computing systems incorporating sensors and machine learning algorithms have been demonstrated to perceive, process, and integrate diverse sensory information where the adaptation and learning can be obtained through dynamically updating the weights of neural network according to the different training algorithms. [19,20] However, on contrary to the distributed processing in biological hierarchical architectures which is more adaptable and cognitive for the optimum analysis of complicated information, the modern computing systems adopting centralized processing are still based on static elements with zeroth-order complexity (e.g., transistor). The essential step for developing neuromorphic systems is to The essential step for developing neuromorphic systems is to construct more biorealistic elementary devices with rich spatiotemporal dynamics to exhibit highly separable responses in dynamic environmental circumstances. Unlike transistor-based devices and circuits with zeroth-order complexity, memristors intrinsically express some simple biomimetic functions. However, with only two-terminal structure, precise control of operation principles to ensure large dynamic space, improved linearity and symmetry, multimodal oper...

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.

The development of artificial intelligence has posed a challenge to machine vision based on conventional complementary metal‐oxide semiconductor (CMOS) circuits owing to its high latency and inefficient power consumption originating from the data shuffling between memory and computation units. Gaining more insight into the function of every part of the visual pathway for visual perception could bring the capabilities of machine vision in terms of robustness and generality. Hardware acceleration of more energy efficient and biorealistic artificial vision highly necessitates neuromorphic devices and circuits which are able to mimic the function of each part of visual pathway. In this paper, we review the structure and function of the entire class of visual neurons from retina to the primate visual cortex within reach (chapter 2). Based on the extraction of biological principles, the recent hardware implemented visual neurons located in different parts of the visual pathway are detailed discussed (chapter 3 and chapter 4). Furthermore, we attempt to provide valuable applications of inspired artificial vision in different scenarios (chapter 5). The functional description of the visual pathway and its inspired neuromorphic devices/circuits are expected to provide valuable insights for the design of next‐generation artificial visual perception systems.This article is protected by copyright. All rights reserved

Electronic Textiles In article number 2301816, Ye Zhou and co‐workers report an all‐textile, all‐organic, washable and breathable sensor to realize wide‐range sensing of human activities and noncontact stimuli without sacrificing biocompatibility and comfortability. Additionally, by combining with the deep‐learning technique, an all‐textile sensing system is established to recognize object shape, contactless trajectory and environmental temperature.