The challenges associated with autonomous information processing and storage will be resolved by neuromorphic computing, which takes inspiration from neural networks in the human brain. To create suitable artificial synaptic devices for artificial intelligence, it is essential to look for approaches to improve device performance. In the present study, we suggest a method to address this problem by inserting an ultrathin AlO X layer at the side of ferroelectric film for the prepared ferroelectric organic effect transistor (Fe-OFET) to modify a ferroelectric polymer film with a low coercive field. The transistors parameters are greatly improved (large memory window exceeding 14 V, high on-off current ratio of 10 3 , and hole mobility up to 10 -2 cm 2 V -1 s -1 ). Furthermore, the optimized high-performance Fe-OFET with 2 nm thickness of AlO X layer is found to have synaptic behaviors including postsynaptic current (PSC), term/long-term plasticity (STP/LTP), spike-amplitude-dependent plasticity (SADP), spike-duration-dependent plasticity (SDDP), paired-pulse facilitation (PPF), spike-rate-dependent plasticity (SRDP), and spike-number-dependent plasticity (SNDP). An outstanding learning accuracy of 87.5% is demonstrated by an imitated artificial neural network made up of Fe-OFET for a big image version of handwritten digits (28 × 28 pixel) from the Modified National Institute of Standards and Technology (MNIST) dataset. By improving synaptic transistor performance in this way, a new generation of neuromorphic computing systems is set to be developed.
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