Highly Efficient Invisible TaO_x/ZTO Bilayer Memristor for Neuromorphic Computing and Image Sensing

Abstract

In today’s new era, multifunctional devices are most prominent due to their compact design, reduction in operating cost, and reduced need of being limited to single functional devices. The electronic synapses and electro-optic functions of the device are such a cornerstone for neuromorphic computing and image sensing applications. In this work, we fabricate a ZTO-based invisible memristor for simulating the human brain for neuromorphic computing and image sensing applications. Long-term potentiation and depressionat least 790repetitive cycles are observed which ensures the synaptic strength. The first-principles density functional theory calculations give insights into the device’s microscopic charge density distribution and switching mechanism. The experimental potentiation and depression data are used to train the Hopfield neural network (HNN) for image recognition of 28 × 28 pixels comprising 784 synapses. The HNN can be successfully trained to identify the input image with a training accuracy of more than 96% in 17 iterations. Furthermore, the device shows excellent highly stable electrical set and optical reset endurance for at least 1500 cycles without degradation, good retention (104 s) at 90 °C, and high transparency (∼85%). This work not only enables us to use our device in artificial intelligence but also provides a significant advantage in the field of image sensing.

Enhanced Synaptic Features of ZnO/TaOx Bilayer Invisible Memristor for Brain-Inspired Computing

“…The concentration of oxygen vacancy in ZnO and TaOx are 35.37 and 20.49 %, respectively. The concentration of oxygen vacancies performs a vital part to construct the filaments in both layers [16,23,24]. We validate the CF model in the device with the basis of oxygen vacancies, as illustrated in Fig.…”

“…4(c). Researchers have described those different quantities of oxygen vacancies create the hourglass shape CF in the bilayer memristors [3,16,23,24]. This hourglass-shaped CF in the device boosts the device's performance.…”

“…Several memristors have been proposed but their poor endurance (repeatable P and D epochs) restricts the devices for neuromorphic computing application [13][14][15][16][17][18][19][20][21]. To enhance the endurance of P and D, we fabricate a fully transparent bilayer ZnO/TaOx memristive synaptic device for the synaptic application.…”

Oxygen Vacancies Controlled Highly Stable Bilayer Analog Synapse Used for Neuromorphic Computing Systems

“…Figure c displays the recalled image that was recovered after the HNN was successfully trained and effectively identified the input image in 23 epochs with a training accuracy of over 92% shown in Figure c,d. In our previous work, detailed explanations of the simulations and other details are provided …”

“…In our previous work, detailed explanations of the simulations and other details are provided. 31 In addition, we investigated the RRAM TiN/TaOx/Pt and TiN/TaOy/Pt single layer devices for comparison shown in Figure 6a,b. Both devices exhibit bipolar RS characteristics with unstable LRS and HRS states.…”