Neuromorphic Computing and High Accuracy (>94%) Pattern Recognition with Cu ₂ SrSnS ₄ -Based RRAM Devices

In the present era of artificial intelligence, devices for smart and fast data computation are crucial for reducing vast amounts of power consumption. The existing von Neumann-based memory devices have limitations due to the discrete storage and computing units; in contrast, RRAM-based memory devices are under consideration as they can potentially surmount the von Neumann bottleneck. In the present work, Cu 2 SrSnS 4 (CSTS)-based RRAM devices, in a Cu/CSTS/ITO configuration, are investigated for their potential to mimic the biological human brain for neuromorphic computing applications. The RRAM device exhibits stable resistive switching for 10 3 current–voltage cycles and retains the resistive state for more than 9.5 × 10 4 seconds. Furthermore, the neuromorphic characteristics, such as synaptic weight, PPF, STDP, and LTP/LTD, have also been successfully investigated together with the impact of humidity (85%) and different temperatures (300–360 K) on device characteristics. The Cu/CSTS/ITO RRAM device exhibits a good postsynaptic current (PSC) under a voltage pulse, demonstrating its capability for analog/digital coding and potential suitability for future spiking neural network (SNN) applications. The devices also exhibited excellent pattern recognition performance for the MNIST data set, with a synaptic device-based accuracy of over 94%, demonstrating the potential of such RRAM devices for neuromorphic computing.