Sub‐Femtojoule‐Energy‐Consumption Conformable Synaptic Transistors Based on Organic Single‐Crystalline Nanoribbons

The highly parallel artificial neural systems based on transistor-like devices have recently attracted widespread attention due to their high-efficiency computing potential and the ability to mimic biological neurobehavior. For the past decades, plenty of breakthroughs related to synaptic transistors have been investigated and reported. In this work, a kind of photoelectronic transistor that successfully mimics the behaviors of biological synapses has been proposed and systematically analyzed. For the individual device, MXenes and the self-assembled titanium dioxide on the nanosheet surface serve as floating gate and tunneling layers, respectively. As the unit electronics of the neural network, the typical synaptic behaviors and the reliable memory stability of the synaptic transistors have been demonstrated through the voltage test. Furthermore, for the first time, the UV-responsive synaptic properties of the MXenes floating gated transistor and its applications, including conditional reflex and supervised learning, have been measured and realized. These photoelectric synapse characteristics illustrate the great potential of the device in bio-imitation vision applications. Finally, through the simulation based on an artificial neural network algorithm, the device successfully realizes the recognition application of handwritten digital images. Thus, this article provides a highly feasible solution for applying artificial synaptic devices to hardware neuromorphic networks.

Section: Resultsmentioning

confidence: 99%

Bio‐Inspired Photoelectric Artificial Synapse based on Two‐Dimensional Ti₃C₂T_x MXenes Floating Gate

Zhao

et al. 2021

“…Besides the various functions and high stability of our synaptic transistor, the energy consumption has also been considered, which is an important feature of artificial synapses. For short‐term plasticity, the energy consumption per spike in Figure 3a is about 4×10 −9 J calculated with the formula I peak × t d × V DS , [ 19,24,26 ] where I peak , t d , and V DS represent the peak value of the EPSC (≈102 nA), the duration time of the pulse (80 ms), and the source–drain voltage (0.5 V), respectively. However, a weaker V DS of 0.05 V and a narrow pulse duration time (0.8 ms) also can induce the response of the device with an EPSC of ≈22 nA, as shown in Figure S8a, Supporting Information.…”

Section: Resultsmentioning

confidence: 99%

“…[ 24 ] reported a flexible electrochemical neuromorphic organic device on polyethylene terephthalate (PET) substrates which is made by commercially available plastic materials. It was found that most devices employs organic materials, [ 24–27 ] transferred 2D materials flake, [ 28–30 ] or amorphous oxide [ 23,31,32 ] as functional compounds based on flexible substrate (polymer, paper and silicon membrane). Poorly, researches on array‐level flexible neuromorphic system has not been reported, which is attributed to the reliability, scalability and stability issues of flexible synaptic devices.…”

Section: Introductionmentioning

confidence: 99%

A Flexible Mott Synaptic Transistor for Nociceptor Simulation and Neuromorphic Computing

Deng

Wang

Liu

et al. 2021

114

Designing transparent flexible electronics with multi‐biological neuronal functions and superior flexibility is a key step to establish wearable artificial intelligence equipment. Here, a flexible ionic gel‐gated VO2 Mott transistor is developed to simulate the functions of the biological synapse. Short‐term and long‐term plasticity of the synapse are realized by the volatile electrostatic carrier accumulation and nonvolatile proton‐doping modulation, respectively. With the achievement of multi‐essential synaptic functions, an important sensory neuron, nociceptor, is perfectly simulated in our synaptic transistors with all key characteristics of threshold, relaxation, and sensitization. More importantly, this synaptic transistor exhibits high tolerance to the bending deformation, and the cycle‐to‐cycle variations of multi‐conductance states in potentiation and depression properties are maintained within 4%. This superior stability further indicates that our flexible device is suitable for neuromorphic computing. Simulation results demonstrate that high recognition accuracy of handwritten digits (>95%) can be achieved in a convolution neural network built from these synaptic transistors. The transparent and flexible Mott transistor based on electrically‐controlled VO2 metal‐insulator transition is believed to open up alternative approaches to developing highly stable synapses for future flexible neuromorphic systems.

“…Some artificial synapses that are operated at comparable power requirements as biological synapses have been successfully designed as well. For instance, an organic synaptic transistor showed superior sensitivity and minimum energy consumption to external stimuli of, as little as, 10 mV [ 123 ] and 0.29 fJ, [ 124 ] respectively. In the future, organic synaptic devices with low energy consumption, multi‐function, high sensitivity, and high‐density integration will be one of promising unit in the artificial intelligent computer of similar to human brain.…”

Section: Discussionmentioning

confidence: 99%

Organic Synaptic Transistors: The Evolutionary Path from Memory Cells to the Application of Artificial Neural Networks

Shao

Zhao

Liu

2021

The progress of neural synaptic devices is experiencing an era of explosive growth. Given that the traditional storage system has yet to overcome the von Neumann bottleneck, it is critical to develop hardware with bioinspired information processing functions and lower power consumption. Transistors based on 2D materials, metal oxides, and organic materials have been adopted to mimic the synapse of a human brain, due to their high plasticity, parallel computing, integrated storage, and system information processing. Among these materials used to build transistors, organic semiconductors are considered to be the most promising candidate for neural synaptic devices and bio‐electronics, owing to their easy processing, mechanical flexibility, low cost, good bio‐compatibility, and ductility. This review focuses on the recent advances in organic synaptic devices with various structures, materials, and working mechanisms. The applications of artificial neural networks that integrate multiple organic synaptic transistors are also concretely discussed. Finally, the challenges that organic synaptic devices currently face are discussed and future developments are forecast.