The limits of transistor scaling and digital architectures are encouraging research into new electronic materials, devices, and systems to meet growing computing demands. In the realm of artificial intelligence, mimicking brain activity for neuromorphic computing is a promising approach. Herein, Ruddlesden–Popper (RP) perovskite‐based flexible and environmentally stable memristors are presented that achieve on‐demand resistive switching between several nonvolatile states by controlling the number of layers and compliance current (CC). The optimal flexible perovskite device based on n = 5 composition, fabricated by complete solution process and measured under ambient conditions without any encapsulation, shows excellent ON/OFF ratio ≈7 × 103, endurance performance (2500 cycles), and robustness to mechanical flexure up to 5 mm bending radii. The role of the physical/chemical reaction at the perovskite–electrode interface is investigated to reveal the origin of the resistive switching in these devices. The primary probing on synaptic characteristics shows stable learning (potentiation and depression) behavior measured up to 19 000 pulses. The invariant paired pulse facilitation index on flat and 5 mm bending radii demonstrates their feasibility for neuromorphic computing applications. The in‐depth analysis also validates the potential of RP‐based memristor devices for applications that require real‐time synaptic processing under extreme mechanical states such as electronic skins.
Modern electronic devices are being developed for cutting-edge applications, as a result of recent developments in artificial intelligence (AI) and machine learning (ML). The demand for “universal memory” devices with exceptional qualities, such as high data transmission speed, storage capacity, non-volatility, and low operation voltage has increased as a result of the industry’s ability to sustain such a high growth rate. In this chapter, we elaborate on the history of the evaluation of novel memristor structures, various switching mechanisms, and materials for developing memristor devices. The current state of the art of the memristor for various applications, such as data storage, artificial synapse, light-induced resistive switching, logic gates, and mimicking human behavior is also systematically summarized.
The conduction of ionic species through the solid-state memristive devices were found to have no comparable mobility with the ions (Na+, K+, and Ca2+) in the human brain creating a major bottleneck for use of these devices for neuromorphic applications. In an attempt to resolve this issue, and compete with demands in soft electronic technologies, ionic electrolytes are promising candidates as active materials. Here, we put forward a new approach of utilizing the rheological properties of Acacia Senegal with Sodium Chloride (AS@NaCl) electrolyte to obtain the resistive switching property. The device exhibits resistive switching, with SET process consuming 0.16mJ, with channel diameter of 1.2cm, and ~ 5.46µJ with 0.2 mm channel diameter. The impedance spectroscopy measurements were performed to investigate the role of the rheological properties of the medium and medium-electrode interface in resistive switching. Furthermore, a theoretical model has been adopted for quantitative analysis. To evaluate the potential of the device for neuromorphic tasks and proving their resemblance with the synapse governing the neuronal dynamics, first accounts of all four of Spike Time Dependent Plasticity (STDP) (Symmetric (Hebbian/Anti-Hebbian), and Asymmetric (Hebbian/Anti-Hebbian)) behavior in addition to other preliminary synaptic characteristics have been presented. The findings presented reveal the potential of the AS@NaCl electrolyte involving low cost and easy processable technology for memristive applications.
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