Damage-free van der Waals metal/NbO_x-NbSe₂ integration for reliable and flexible memristor in neuromorphic computing

Ultrathin native oxides formed on two-dimensional (2D) metallic transition metal dichalcogenides (MTMDs), such as NbSe₂, have recently emerged as promising active switching layers for memristive devices. However, these oxides are highly susceptible to surface damage during conventional top-electrode deposition processes, which leads to interfacial disorder and degraded switching reliability. Here, we demonstrate a highly reliable and flexible NbO_x-NbSe₂ memristor utilizing a damage-free van der Waals (vdW) metal transfer technique. By gently laminating a prefabricated gold (Au) electrode onto the NbO_x-NbSe₂, a pristine and chemically undisturbed metal/oxide interface is achieved. While metallization-induced barrier damage causes a large OFF-state direct tunneling (DT) current in the evaporated-metal memristor (Ev-MEM), preserving the pristine oxide interface in the transferred-metal memristor (T-MEM) effectively suppresses this OFF-state DT current. Consequently, the T-MEM exhibits an ON/OFF ratio of 10⁵-a 1,000 times improvement over the Ev-MEM (10²)-along with a stable retention time of > 10⁴ s and an endurance exceeding 10³ cycles. Furthermore, the T-MEM exhibits exceptional synaptic linearity (β = 0.6) and a full dynamic range (0-1), in contrast to the Ev-MEM, which suffers from poor linearity (β = 3.1) and a limited dynamic range (0.2-1). As a result, a hardware-aware convolutional neural network (CNN) simulation shows a near-ideal MNIST image recognition accuracy of 98.1% for the T-MEM based system, significantly outperforming the Ev-MEM counterpart (75.8%). In addition, capitalizing on the inherent flexibility of the 2D NbSe₂ and the vdW interface, devices fabricated on a polymer substrate demonstrate remarkable mechanical robustness, maintaining stable switching characteristics over 200 bending cycles at a 30° angle. Our findings establish that damage-free vdW metal integration is a crucial prerequisite for developing reliable, highly accurate, and flexible neuromorphic hardware.