GeTe/MoTe2 Van der Waals Heterostructures: Enabling Ultralow Voltage Memristors for Nonvolatile Memory and Neuromorphic Computing Applications.

Advanced electronic semiconducting Van der Waals heterostructures (HSs) are promising candidates for exploring next-generation nanoelectronics owing to their exceptional electronic properties, which present the possibility of extending their functionalities to diverse potential applications. In this study, GeTe/MoTe2 HS are explored for nonvolatile memory and neuromorphic-computing applications. Sputter-deposited Ag/GeTe/MoTe2/Pt HS cross-point devices are fabricated, and they demonstrate memristor behavior at ultralow switching voltages (VSET: 0.15 V and VRESET: -0.14 V) with very low energy consumption (≈30 nJ), high memory window, long retention time (104 s), and excellent endurance (105 cycles). Resistive switching is achieved by adjusting the interface between the Ag top electrode and the heterojunction switching layer. Cross-sectional transmission electron microscope images and conductive atomic force microscopy analysis confirm the presence of a conducting filament in the heterojunction switching layer. Further, emulating various synaptic functions of a biological synapse reveals that GeTe/MoTe2 HS can be utilized for energy-efficient neuromorphic-computing applications. A multilayer perceptron is implemented using the synaptic weights of the Ag/GeTe/MoTe2/Pt HS device, revealing high pattern accuracy (81.3%). These results indicate that HS devices can be considered a potential solution for high-density memory and artificial intelligence applications.

Review of Electrochemically Synthesized Resistive Switching Devices: Memory Storage, Neuromorphic Computing, and Sensing Applications.

Resistive-switching-based memory devices meet most of the requirements for use in next-generation information and communication technology applications, including standalone memory devices, neuromorphic hardware, and embedded sensing devices with on-chip storage, due to their low cost, excellent memory retention, compatibility with 3D integration, in-memory computing capabilities, and ease of fabrication. Electrochemical synthesis is the most widespread technique for the fabrication of state-of-the-art memory devices. The present review article summarizes the electrochemical approaches that have been proposed for the fabrication of switching, memristor, and memristive devices for memory storage, neuromorphic computing, and sensing applications, highlighting their various advantages and performance metrics. We also present the challenges and future research directions for this field in the concluding section.

Flexible Memristive Organic Solar Cell Using Multilayer 2D Titanium Carbide MXene Electrodes.

Hybrid systems have attracted significant attention within the scientific community due to their multifunctionality, which has resulted in increasing demands for wearable electronics, green energy, and miniaturization. Furthermore, MXenes are promising two-dimensional materials that have been applied in various areas due to their unique properties. Herein, a flexible, transparent, and conductive electrode (FTCE) based on a multilayer hybrid MXene/Ag/MXene structure that can be applied to realize an inverted organic solar cell (OSC) with memory and learning functionalities is reported. This optimized FTCE exhibits high transmittance (84%), low sheet resistance (9.7 Ω sq-1 ), and reliable operation (even after 2000 bending cycles). Moreover, the OSC using this FTCE achieves a power conversion efficiency of 13.86% and sustained photovoltaic performance, even after hundreds of switching cycles. The fabricated memristive OSC (MemOSC) device also exhibits reliable resistive switching behavior at low operating voltages of 0.60 and -0.33 V (similar to biological synapses), an excellent ON/OFF ratio (103 ), stable endurance performance (4 × 103 ), and memory retention properties (104 s). Moreover, the MemOSC device can mimic synaptic functionalities on a biological time scale. Thus, MXene can potentially be used as an electrode for highly efficient OSCs with memristive functions for future intelligent solar cell modules.

Unraveling the Effect of the Water Content in the Electrolyte on the Resistive Switching Properties of Self-Assembled One-Dimensional Anodized TiO2 Nanotubes.

The applied potential, time, and water content are crucial factors in the electrochemical anodization process because the growth of one-dimensional nanotubes can be accelerated by enhancing the corrosive effect. We investigated the effect of the water content on the resistive switching (RS) properties of Ti foils by anodizing the foils and varying the water content in an electrolyte (1-10 vol %). By increasing the water content, we facilitated a slow transition from nanopores to nanotubes and realized an increase in the tube wall diameter and tube length. All of the fabricated memristive devices exhibited a reliable and reproducible bipolar resistive switching effect. The optimized device exhibited bipolar RS properties with good dc endurance (104 cycles) and data retention capability (105 s). Our results suggest that as the water content increases to 5 vol %, the RS process improves; further increases in the water content impair the RS process.

Amorphous Boron Nitride Memristive Device for High-Density Memory and Neuromorphic Computing Applications.

Although two-dimensional (2D) nanomaterials are promising candidates for use in memory and synaptic devices owing to their unique physical, chemical, and electrical properties, the process compatibility, synthetic reliability, and cost-effectiveness of 2D materials must be enhanced. In this context, amorphous boron nitride (a-BN) has emerged as a potential material for future 2D nanoelectronics. Therefore, we explored the use of a-BN for multilevel resistive switching (MRS) and synaptic learning applications by fabricating a complementary metal-oxide-semiconductor (CMOS)-compatible Ag/a-BN/Pt memory device. The redox-active Ag and boron vacancies enhance the mixed electrochemical metallization and valence change conduction mechanism. The synthesized a-BN switching layer was characterized using several analyses. The fabricated memory devices exhibited bipolar resistive switching with low set and reset voltages (+0.8 and -2 V, respectively) and a small operating voltage distribution. In addition, the switching voltages of the device were modeled using a time-series analysis, for which the Holt's exponential smoothing technique provided good modeling and prediction results. According to the analytical calculations, the fabricated Ag/a-BN/Pt device was found to be memristive, and its MRS ability was investigated by varying the compliance current. The multilevel states demonstrated a uniform resistance distribution with a high endurance of up to 104 direct current (DC) cycles and memory retention characteristics of over 106 s. Conductive atomic force microscopy was performed to clarify the resistive switching mechanism of the device, and the likely mixed electrochemical metallization and valence change mechanisms involved therein were discussed based on experimental results. The Ag/a-BN/Pt memristive devices mimicked potentiation/depression and spike-timing-dependent plasticity-based Hebbian-learning rules with a high pattern accuracy (90.8%) when implemented in neural network simulations.

Work Function-Tunable Amorphous Carbon-Silver Nanocomposite Hybrid Electrode for Optoelectronic Applications.

Parameters such as electrode work function (WF), optical reflectance, electrode morphology, and interface roughness play a crucial role in optoelectronic device design; therefore, fine-tuning these parameters is essential for efficient end-user applications. In this study, amorphous carbon-silver (C-Ag) nanocomposite hybrid electrodes are proposed and fully characterized for solar photovoltaic applications. Basically, the WF, sheet resistance, and optical reflectance of the C-Ag nanocomposite electrode are fine-tuned by varying the composition in a wide range. Experimental results suggest that irrespective of the variation in the graphite-silver composition, smaller and consistent grain size distributions offer uniform WF across the electrode surface. In addition, the strong C-Ag interaction in the nanocomposite enhances the nanomechanical properties of the hybrid electrode, such as hardness, reduced modulus, and elastic recovery parameters. Furthermore, the C-Ag nanocomposite hybrid electrode exhibits relatively lower surface roughness than the commercially available carbon paste electrode. These results suggest that the C-Ag nanocomposite electrode can be used for highly efficient photovoltaics in place of the conventional carbon-based electrodes.

Ti3C2-Based MXene Oxide Nanosheets for Resistive Memory and Synaptic Learning Applications.

MXene, a new state-of-the-art two-dimensional (2D) nanomaterial, has attracted considerable interest from both industry and academia because of its excellent electrical, mechanical, and chemical properties. However, MXene-based device engineering has rarely been reported. In this study, we explored Ti3C2 MXene for digital and analog computing applications by engineering the top electrode. For this purpose, Ti3C2 MXene was synthesized by a simple chemical process, and its structural, compositional, and morphological properties were studied using various analytical tools. Finally, we explored its potential application in bipolar resistive switching (RS) and synaptic learning devices. In particular, the effect of the top electrode (Ag, Pt, and Al) on the RS properties of the Ti3C2 MXene-based memory devices was thoroughly investigated. Compared with the Ag and Pt top electrode-based devices, the Al/Ti3C2/Pt device exhibited better RS and operated more reliably, as determined by the evaluation of the charge-magnetic property and memory endurance and retention. Thus, we selected the Al/Ti3C2/Pt memristive device to mimic the potentiation and depression synaptic properties and spike-timing-dependent plasticity-based Hebbian learning rules. Furthermore, the electron transport in this device was found to occur by a filamentary RS mechanism (based on oxidized Ti3C2 MXene), as determined by analyzing the electrical fitting curves. The results suggest that the 2D Ti3C2 MXene is an excellent nanomaterial for non-volatile memory and synaptic learning applications.