Chaoyue Zheng,Yuan Liao,Junjie Wang,Ye Zhou,Su-Ting Han
The key to the study of flexible neuromorphic computing is the excellent weight update characteristic of neuromorphic devices. Electric-double-layer transistors (EDLTs) include high transconductance, excellent stability of threshold voltage, linear weight updates, and repetitive ion-concentration-dependent switching properties. However, up to now, there is no report on a flexible EDLT that provides all the aforementioned performance characteristics. Here, a planar flexible floating-gate EDLT including an excellent linear/symmetric weight update, a large number (>800) of conductance states, repetitive switching endurance (>100 cycles), and low variation in weight update is reported. After 800 signal stimulations, it is found that the nonlinearity values of LTP are between 0.20 and 0.85, those of LTD fall between 0.66 and 1.55, the symmetricity values are between 120.7 and 639.8, and the dynamic range is between 150 and 352 nS. The study of 8 × 8 flexible floating-gate EDLT arrays shows that the average deviation and standard deviation between the experimental and theoretical values are 1.36 and 1.93, respectively, indicating that the conductance regulation in the array has a relatively small deviation. The different bending angles and the mechanical stability of the floating-gate EDLT are also studied, which exhibit the excellent bending properties. Furthermore, we studied the recognition of MNIST handwritten digit images by a three-layer perceptron artificial neural network with the experimental weight update, and the maximal recognition accuracy is up to 87.8%.
The limitations of von Neumann architectures in computational speed and energy efficiency have driven the development of neuromorphic computing systems, where optoelectronic synaptic devices play a pivotal role in enabling brain-inspired information processing. This study presents an optoelectronic synaptic device based on a two-dimensional conductive metal-organic framework material Cu-BHT which exhibits broad visible-light absorption (300 - 600 nm), high photoconductivity, and solution processability. The fabricated device with a planar structure of Ag/Cu-BHT/Ag successfully simulates the key biological synaptic functionalities, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and short-to-long-term memory transitions. The device achieves a high accuracy of 96.3% in MNIST handwritten digit recognition using a convolutional neural network (CNN). Furthermore, multi-wavelength response of the device enables optical logic operations (AND/OR) and associative learning paradigms, such as Pavlovian conditioning experiment. This work underscores Cu-BHT as a versatile material for neuromorphic computing and artificial visual systems, with future research exploring scalable fabrication and hybrid integration to realize adaptive vision platforms with enhanced energy efficiency.
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