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Brain-like neuromorphic computing networks based on the human brain information processing model are gradually breaking down the memory barriers caused by traditional computing frameworks. The brain-like neural system consists of electronic synapses and neurons. The multiple ferroelectric polarization switching modulated by the external electric field is well suited to simulate artificial neural synaptic weights. Therefore, ferroelectric diodes' (FDs) synapses have great advantages in building highly reliable and energy-efficient artificial neural networks. In this paper, we demonstrate the FDs synapse, which is based on rare-earth metal-doped BaTiO3 ferroelectric dielectric layer materials. This performs short-term and long-term synaptic plasticity behaviors by modulating synaptic weights using pulsed stimuli to polarize or flip ferroelectric films. In addition, convolutional neural networks were constructed on the MNIST dataset and the Fashion-MNIST dataset to check the feasibility of the device in simulating bio-visual recognition. The results expand the application of FDs' devices in the intersection of artificial intelligence and bioscience.

With the data explosion in the intelligent era; the traditional von Neumann computing system is facing great challenges of storage and computing speed. Compared to the neural computing system, the traditional computing system has higher consumption and slower speed. However; the feature size of the chip is limited due to the end of Moore’s Law. An artificial synapse based on halide perovskite CsPbI3 was fabricated to address these problems. The CsPbI3 thin film was obtained by a one-step spin-coating method, and the artificial synapse with the structure of Au/CsPbI3/ITO exhibited learning and memory behavior similar to biological neurons. In addition, the synaptic plasticity was proven, including short-term synaptic plasticity (STSP) and long-term synaptic plasticity (LTSP). We also discuss the possibility of forming long-term memory in the device through changing input signals.

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