A bioinspired flexible neuromuscular system based thermal-annealing-free perovskite with passivation

Liu, Jiaqi; Gong, Jue; Wei, Huanhuan; Li, Yameng; Wu, Haixia; Jiang, Chengpeng; Li, Yuelong; Xu, Wentao

doi:10.1038/s41467-022-35092-w

Cited by 37 publications

(25 citation statements)

References 57 publications

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“…So far, many materials, device-design concepts, and working mechanisms have been intensively studied to simulate synaptic plasticity, achieve brain-like intelligence and develop more effective sensing and information-processing methods. [2,3,39,53,79,[99][100][101][102][103][104] In this part, we focus on introducing iontronic neural devices, i.e., iontronic transistors and iontronic memristors, including their main materials components, device structures, and working principles.…”

Section: Intronic Neural Devicesmentioning

confidence: 99%

Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

Dai

Liu

et al. 2023

Advanced Materials

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Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic‐electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron‐based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low‐level and high‐level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.

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Section: Intronic Neural Devicesmentioning

confidence: 99%

Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

Dai

Liu

et al. 2023

Advanced Materials

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“…Recently, methylammonium lead triiodide (MAPbI 3 ) based synaptic devices exhibiting synaptic behaviors concerning photonic and electronic stimulations were reported. [5,6,10,11] However, due to the unfavorable intrinsic phase transition and poor thin film surface morphology, very few attempts were made with formamidinium cation-based perovskites for their neuromorphic properties.…”

Section: Introductionmentioning

confidence: 99%

Low Switching Power Neuromorphic Perovskite Devices with Quick Relearning Functionality

Assi

Karthikeyan

et al. 2023

Adv Elect Materials

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In the quest to reduce energy consumption, there is a growing demand for technology beyond silicon as electronic materials for neuromorphic artificial intelligence devices. Equipped with the criteria of energy efficiency and excellent adaptability, organohalide perovskites can emulate the characteristics of synaptic functions in the human brain. In this aspect, this study designs and develops CsFAPbI3‐based memristive neuromorphic devices that can switch at low power and show larger endurance by adopting the powder engineering methodology. The neuromorphic characteristics of the CsFAPbI3‐based devices exhibit an ultra‐high paired‐pulse facilitation index for an applied electric stimuli pulse. Moreover, the transition from short‐term to long‐term memory requires ultra‐low energy with long relaxation times. The learning and training cycles illustrate that the CsFAPbI3‐based devices exhibit faster learning and memorization process owing to their larger carrier lifetime compared to other perovskites. The results provide a pathway to attain low‐power neuromorphic devices that are synchronic to the human brain's performance.

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“…Owing to the exponential expansion in the network parameters with the increasing complexity of artificial neural networks, neuromorphic computing has attracted significant attention. [1][2][3] Recent research on neuromorphic computing focuses on imitating biological systems such as the neuromuscular system, 4 efferent nerves, 5 and neurotransmitters, 6 as well as developing elements that accelerate software-based neural networks. 7,8 Neuromorphic computing for accelerating analogue multiplyand-accumulation (MAC) operations minimises data transfer by corresponding memory elements as artificial synapses of the neural network.…”

Section: Introductionmentioning

confidence: 99%