Abstract
The growing demand for energy-efficient computing in artificial intelligence requires novel memory technologies capable of storing and processing information. Memristors stand out in thanks to their ability to store information, mimic synaptic behavior and support in-memory computing architectures while requiring minimal active areas and energy consumptions. Here is presented a scalable and cost-effective approach to fabricate Ag/MoS(2)/Au memristors as resistive switching memory devices by combining roll-to-roll mechanical exfoliation of two-dimensional materials with inkjet printing. These devices exhibit reliable non-volatile switching behavior attributed to the formation and dissolution of metallic conductive filaments within the MoS(2) layer, with high resistance ratios and robust retention times. A fully-connected neural networks is simulated using quantized weights mapped onto a virtual memristor crossbar array demonstrating that classification tasks can be performed with high accuracy even with limited bit-width precision, highlighting the potential of these devices for energy-efficient, high-throughput AI hardware.