Enhanced GaSe-Based Vertical Memristors with HfO₂ Layer for High-Performance Neuromorphic Computing

In neuromorphic computing, gallium selenide (GaSe)-based memristors have emerged as promising candidates for energy-efficient and high-performance synaptic devices due to their inherent ability to emulate synaptic plasticity. However, the switching ratio (SR) of vertically structured GaSe-based memristors is often limited, restricting their effectiveness in neural network weight mapping. In this study, we introduce an improved GaSe vertical memristor featuring a copper (Cu) top electrode and a gold (Au) bottom electrode. By incorporating a thin hafnium oxide (HfO 2 ) layer between the GaSe material and the bottom electrode, we significantly enhance the SR and expand the number of available conductive states and the conductance modulation range. The I ON/OFF ratio rises with increasing HfO 2 layer thickness, achieving values of 30, 3.9 × 10 4, and 5.9 × 10 5 for thicknesses of 1, 2, and 3 nm, respectively. However, as the HfO 2 layer thickness increases from 1 to 2 or 3 nm, the GaSe vertical memristor shifts from analog to digital behavior, resulting in heightened conductance nonlinearity. Comparing the performance of GaSe vertical memristors with varying HfO 2 layer thicknesses, the device with a 1 nm HfO 2 layer stands out, offering both an improved I ON/OFF ratio and an optimized synaptic linearity factor. Furthermore, simulations conducted on the “NeuroSim+” platform demonstrate that a three-layer neural network leveraging this optimized GaSe vertical memristor achieves an impressive handwritten digit recognition accuracy of 93%. This study establishes a solid foundation for the development of high-performance GaSe memristors with improved switching characteristics for future neuromorphic applications.