Synaptic Properties of a PbHfO Ferroelectric Memristor for Neuromorphic Computing.

The conventional von Neumann architecture has proven to be inadequate in keeping up with the rapid progress in artificial intelligence. Memristors have become the favored devices for simulating synaptic behavior and enabling neuromorphic computations to address challenges. An artificial synapse utilizing the perovskite structure PbHfO (PHO) has been created to tackle these concerns. By employing the sol-gel technique, a ferroelectric film composed of Au/PHO/FTO was created on FTO/glass for the purpose of this endeavor. The artificial synapse is composed of Au/PHO/FTO and exhibits learning and memory characteristics that are similar to those observed in biological neurons. The recognition accuracy for both MNIST and Fashion-MNIST data sets saw an increase, reaching 92.93% and 76.75%, respectively. This enhancement resulted from employing a convolutional neural network architecture and implementing an improved stochastic adaptive algorithm. The presented findings showcase a viable approach to achieve neuromorphic computation by employing artificial synapses fabricated with PHO.