All Solution-Processed Inorganic, Multilevel Memristors Utilizing Liquid Metals Electrodes Suitable for Analog Computing

Abstract

Herein, we report a solution-processable memristive device based on bismuth vanadate (BiVO 4 ) and titanium dioxide (TiO 2 ) with gallium-based eutectic gallium−indium (EGaIn) and gallium−indium-tin alloy (GaInSn) liquid metal as the top electrode. Scanning electron microscopy (SEM) shows the formation of a nonporous structure of BiVO 4 and TiO 2 for efficient resistive switching. Additionally, the gallium-based liquid metal (GLM)contacted memristors exhibit stable memristor behavior over a wide temperature range from −10 to +90 °C. Gallium atoms in the liquid metal play an important role in the conductive filament formation as well as the device's operation stability as elucidated by I−V characteristics. The synaptic behavior of the GLM-memristors was characterized, with excellent long-term potentiation (LTP) and longterm depression (LTD) linearity. Using the performance of our device in a multilayer perceptron (MLP) network, a ∼90% accuracy in the handwriting recognition of modified national institute of standards and technology database (MNIST) was achieved. Our findings pave a path for solution-processed/GLM-based memristors which can be used in neuromorphic applications on flexible substrates in a harsh environment.

High-Density, Nonvolatile, Flexible Multilevel Organic Memristor Using Multilayered Polymer Semiconductors

“…The memory effect exhibits nonvolatility, implying the stability of data retention and in the absence of an applied voltage. In general, organic memristors operate as binary systems, characterized by two distinct conductive states: the 'ON' and 'OFF' states. , However, employing a multilevel memory strategy for high-density data storage [≥3 n ; where n = number of bits in a device] emerges as a proficient alternative, − enabling the capability to store more than two distinct states within the same device. These devices are energy efficient and offer a faster access time by accessing multiple states simultaneously.…”

Improved Performance of HfxZnyO‐Based RRAM and its Switching Characteristics down to 4 K Temperature

“…To facilitate more extensive implementation of next-generation in-memory-computing systems, resistive random-access memory (RRAM) is widely considered to be one of the most promising building blocks. [1][2][3][4][5][6][7] In recent years, metal-oxidesbased RRAMs have been implemented in an artificial neural network with considerable success, [8][9][10][11] but with performance metrics that can be further improved. In addition, with the recent rise of quantum applications, the use of resistive Many attempts have been studied to investigate the potential mechanism of doping and, thus, to pursue better performance including voltages, stability, multi-states storage capability as well as synaptic behavior.…”

Analog HfxZr1‐xO2Memristors with Tunable Linearity for Implementation in a Self‐Organizing Map Neural Network

“…Despite the promises, state of the art mono‐oxide memristors still falls short in areas such as poor linearity modulation and high operating voltages. [ 2 ] A potential avenue for further performance optimization can be via a controlled approach in introducing new elements to form binary or higher‐order oxide memristors. In recent years, the growth of binary or multi‐element metal oxides can easily be achieved in many advanced manufacturing technologies.…”