Perovskite-based memristors have attracted much attention in synaptic simulation due to their outstanding electrical properties and promising potential in neuromorphic computing (NC). In this work, inorganic lead-free perovskite-based memristors composed of Ag/Cs 3 Bi 2−x Li x I 9−2x (CBL x I)/ITO (x = 0, 0.2, 0.4, 0.6) are fabricated, and the electrical properties, such as endurance, on/off ratio, and retention time, are determined. It is found that the device with x = 0.4 shows good characteristics, such as a set voltage of −0.1 V and a retention time of 10 4 s. The multilevel storage performance is investigated, and multiple synaptic characteristics, such as paired-pulse facilitation (PPF), spike-voltage-dependent plasticity (SVDP), spike-width-dependent plasticity (SWDP), spike-timing-dependent plasticity (STDP), and learning−forgetting, are simulated. The conductive mechanism of the device is analyzed and discussed with an analogy to natural volcanic rocks, which also have a large surface area, high adsorption, and high chemical inertness. An artificial neural network (ANN) based on the potentiation/depression characteristics is designed and analyzed theoretically, and a pattern recognition rate of 94.25% is accomplished. The strategy and results described in this paper provide insights into the development of nonvolatile memory devices boding well for the adoption of neuromorphic computing for image recognition.
Brain-inspired neuromorphic computing is a promising way to implement artificial intelligence to overcome the issues of independent information processing and storage. An artificial synaptic device with tunable plasticity can perform learning and memorization by adjusting the weight of the synapse. In this work, a synaptic memristor composed of porous silicon oxide (PSiOx) incorporated with MoS2 quantum dots (QDs) is fabricated and excitatory paired-pulse facilitation (PPF), post-tetanic potentiation (PTP), learning-forgetting behavior, and spike-timing-dependent plasticity (STDP) are demonstrated. The short/long-term plasticity (SLTP) in biological synapses reveal the possibility of tunable synaptic plasticity with self-regulating functions under a series of excitation frequency between 200 μs and 10 ms. An artificial neural network (ANN) is designed theoretically according to the SLTP characteristic curves of the synapses and the recognition rate is observed to increase from 54.2% to 91.8% by simply adjusting the input frequency. The image recognition accuracy is improved by 6% in the presence of 20% noise at an input frequency of 1 ms. The excellent results and novel strategy reveal an important step for image recognition in next-generation neuromorphic computing systems.
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