Research Projects

The von Neumann computing system, which limits the efficiency of conventional computing systems due to data-transfer bottlenecks and feature-size limitations, is being replaced by brain-inspired neuromorphic computing systems. These systems offer in-memory computing attributes, high efficiency, adaptability, and parallel signal processing. Synaptic plasticity allows for processing learning and memory functions by altering synaptic strength, making electronic synaptic devices based on solid-state memristors promising for neuromorphic computing. Several transition metal oxides, metal dichalcogenides, and hybrid polymer/inorganic nanocomposite have been investigated for resistive switching applications. Lead halide perovskites (HPs) have gained attention due to their properties, such as high charge carrier mobility, small binding energy, and long carrier diffusion lengths. However, HPs with organic cations suffer from thermal instability and toxicity. Flexible neuromorphic devices are also attractive due to the possibilities for developing brain-inspired multi-functional perception learning systems. This study aims to fabricate lead-free halide perovskite-based memristor devices to mimic the synaptic functions of biological synapses. Electrical and structural characteristics will be investigated for the development of prototype devices, and the mechanical flexibility of the halide perovskite-based synaptic devices will be confirmed under bending conditions for flexible electronics.