To construct an artificial intelligence system with high efficient information integration and computing capability like the human brain, it is necessary to realize the biological neurotransmission and information processing in artificial neural network (ANN), rather than a single electronic synapse as most reports. Because the power consumption of single synaptic event is ∼10 fJ in biology, designing an intelligent memristors-based 3D ANN with energy consumption lower than femtojoule-level (e.g., attojoule-level) and faster operating speed than millisecond-level makes it possible for constructing a higher energy efficient and higher speed computing system than the human brain. In this paper, a flexible 3D crossbar memristor array is presented, exhibiting the multilevel information transmission functionality with the power consumption of 4.28 aJ and the response speed of 50 ns per synaptic event. This work is a significant step toward the development of an ultrahigh efficient and ultrahigh-speed wearable 3D neuromorphic computing system.

With the development and application of artificial intelligence, there is an appeal to the exploitation of various sensors and memories. As the most important perception of human beings, vision occupies more than 80% of all the received information. Inspired by biological eyes, an artificial retina based on 2D Janus MoSSe was fabricated, which could simulate functions of visual perception with electronic/ion and optical comodulation. Furthermore, inspired by human brain, sensing, memory, and neuromorphic computing functions were integrated on one device for multifunctional intelligent electronics, which was beneficial for scalability and high efficiency. Through the formation of faradic electric double layer (EDL) at the metal-oxide/electrolyte interfaces could realize synaptic weight changes. On the basis of the optoelectronic performances, light adaptation of biological eyes, preprocessing, and recognition of handwritten digits were implemented successfully. This work may provide a strategy for the future integrated sensing-memory-processing device for optoelectronic artificial retina perception application.

which consume 10 fJ per synaptic event. Previous reports have proposed various artificial synaptic devices for simulating long-term and short-term plasticity. [5][6][7][8][9] Although the development of artificial synaptic architectures has achieved energy consumption down to the femtojoule level, [10] it is hard to achieve lower levels due to the slow response time and highlevel post-synaptic current (PSC). These problems lead to a bottleneck in reducing the power of synaptic weights update. Hence, designing an appropriate device with high-speed program and low-level current are necessary for solving energy consumption problems.Most previous reports were limited to single devices with simple connections and pre-and post-synaptic learning rules, such as excitatory post-synaptic current (EPSC), inhibitory post-synaptic current (IPSC), short-term plasticity (STP), paired pulse facilitation/depression (PPF/PPD), spike time-dependent plasticity (STDP), learning-forgetting-relearning behaviors etc. [11][12][13] The global synapses in neural networks with interconnections to tens of thousands of other synapses for information transmission by neurotransmitters are fundamental in massive parallelism, but are usually overlooked. In contrast to homosynapse, heterosynapse is a basic part of simplified global neural network and have multi-terminal configurations, including pre-, post-, and modulatory-synapse (mod-synapse). [14] Heterosynapse plays key roles in biological functions, including long-term memory for synaptic growth and associative learning. [15,16] Therefore, it is important to simulate synaptic features with responses to modu latory terminal. As a novel gate-control mode, optical control could realize multi-terminal neuromodulations in heterosynapse, with the advantages of reduced thermal loss and simple operating mode. To better understand the complex global neuromodulations in heterosynapse, optoelectronic materials should be meticulously selected for multi-terminal synapses' construction.In another aspect, 2D transition metal dichalcogenides (TMDCs) have attracted increasing attention as promising candidates for next-generation flexible multifunctional electronics due to their superior mechanical flexibility, unique electrical, and optical properties. [17][18][19][20] For example, MoS 2based multi-bit memory exhibits photoelectronic non-volatile Although the energy consumption of reported neuromorphic computing devices inspired by biological systems has become lower than traditional memory, it still remains greater than bio-synapses (≈10 fJ per spike). Herein, a flexible MoS 2 -based heterosynapse is designed with two modulation modes, an electronic mode and a photoexcited mode. A one-step mechanical exfoliation method on flexible substrate and low-temperature atomic layer deposition process compatible with flexible electronics are developed for fabricating wearable heterosynapses. With a pre-spike of 100 ns, the synaptic device exhibits ultralow energy consumption of 18.3 aJ per spike in long-term potentiat...

No abstract

Neuromorphic computing memristors are attractive to construct low-power- consumption electronic textiles due to the intrinsic interwoven architecture and promising applications in wearable electronics. Developing reconfigurable fiber-based memristors is an efficient method to realize electronic textiles that capable of neuromorphic computing function. However, the previously reported artificial synapse and neuron need different materials and configurations, making it difficult to realize multiple functions in a single device. Herein, a textile memristor network of Ag/MoS2/HfAlOx/carbon nanotube with reconfigurable characteristics was reported, which can achieve both nonvolatile synaptic plasticity and volatile neuron functions. In addition, a single reconfigurable memristor can realize integrate-and-fire function, exhibiting significant advantages in reducing the complexity of neuron circuits. The firing energy consumption of fiber-based memristive neuron is 1.9 fJ/spike (femtojoule-level), which is at least three orders of magnitude lower than that of the reported biological and artificial neuron (picojoule-level). The ultralow energy consumption makes it possible to create an electronic neural network that reduces the energy consumption compared to human brain. By integrating the reconfigurable synapse, neuron and heating resistor, a smart textile system is successfully constructed for warm fabric application, providing a unique functional reconfiguration pathway toward the next-generation in-memory computing textile system.