With the demand for low‐power‐operating artificial intelligence systems, bio‐inspired memristor devices exhibit potential in terms of high‐density memory functions and the emulation of the synaptic dynamics of the human brain. The 2D material MXene attracts considerable interest for use in resistive‐switching memory and artificial synapse devices owing to its excellent physicochemical properties in memristor devices. However, few memristive and synaptic MXene devices that display increased switching performances are reported, with no significant results. Herein, the conductivity of MXene (Ti3C2Tx) is engineered via etching and oxidation to enhance the switching performance of the device. The exceptional properties of partially oxidized MXene memristors include large memory windows and low threshold biases, and the complex spike‐timing‐dependent plasticity synaptic rules are also emulated. The low threshold potential distribution, reliable retention time (104 s), and distinct resistance states with a high ON–OFF ratio (>104) are the main memory‐related features of this device. The experimentally determined switching potentials of the optimized device are also uniformly distributed, according to a statistical probability‐based approach. This investigation may promote the essential material properties for use in high‐density non‐volatile memory storage and artificial synapse systems in the field of innovative nanoelectronic devices.
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.
The rise of artificial intelligence and machine learning demands versatile electronic devices for memory and braininspired computing applications. The electronic materials are the backbones of these applications. Considering this, a functional Co x (PO 4 ) 2 nanomaterial was synthesized for resistive memory and neuromorphic computing applications. The synthesized nanomaterial was well characterized by using X-ray diffraction, Fourier transform infrared spectroscopy, field emission-scanning electron microscopy, and X-ray photoelectron spectroscopy. The fabricated Ag/Co x (PO 4 ) 2 /ITO device shows bipolar resistive switching and memristive properties. The SET and RESET voltages were analyzed by using different statistical measures, and their distribution was studied by using the Weibull technique. The results suggested that the SET voltages were more uniformly distributed than the RESET voltage. The switching nonlinearity was modeled and predicted by using Holt's exponential smoothingbased statistical time series analysis method. In the case of nonvolatile memory tests, the device shows good endurance (10 3 cycles) and memory retention (3 × 10 4 s) with excellent memory window (1.7 × 10 3 ) properties. Moreover, the device can mimic the potentiation−depression and spike-timing-dependent plasticity-based Hebbian learning rules, suggesting Co x (PO 4 ) 2 is a potential nanomaterial for the fabrication of artificial synapse. The detailed analysis of electrical results suggested that the space-charge-limited current-based charge transport was responsible for the device conduction, whereas the formation and rupture of conductive filament(s) were responsible for the resistive switching in the Ag/Co x (PO 4 ) 2 /ITO memristive device. The results of the present investigation suggested that the Co x (PO 4 ) 2 nanomaterial is a potential candidate for resistive memory and brain-inspired computing applications
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