Artificial Neural Network Based on Doped HfO2 Ferroelectric Capacitors With Multilevel Characteristics

We propose an electronic synapse based on an Al: HfO ₂ metal-ferroelectric-metal (MFM) capacitor with multi-level characteristics. The device demonstrates excellent multilevel behavior, linearity, weight update symmetry, and low static power consumption. Furthermore, an artificial neural network (ANN) based on this capacitor is built and evaluated through SPICE and MATLAB simulations. The performance indicates that more than 78% accuracy and nJ/ms dynamic speed-energy efficiency in recognizing the standard MNIST handwritten digits are achieved, showing the capability of high-density integration and low energy consumption of the capacitive ANNs.