In recent years, the synaptic properties of transistors have been extensively studied. Compared with liquid or organic material-based transistors, inorganic solid electrolyte-gated transistors have the advantage of better chemical stability. This study uses a simple, low-cost solution technology to prepare In2O3 transistors gated by AlLiO solid electrolyte. The electrochemical performance of the device is achieved by forming a double electric layer and electrochemical doping, which can mimic basic functions of biological synapses, such as excitatory postsynaptic current (EPSC), paired-pulse promotion (PPF), and spiking time-dependent plasticity (STDP). Furthermore, complex synaptic behaviors such as Pavlovian classical conditioning and the Morse code "QINGDAO" are successfully emulated. With a 95 % identification accuracy, an artificial neural network based on transistors is built to recognize sign language and enable sign language interpretation. Additionally, the handwriting digit's identification accuracy is 94 %. Even with various levels of Gaussian noise, the recognition rate is still above 84 %. The above findings demonstrate the potential of In2O3/AlLiO TFT in shaping the next generation of artificial intelligence.
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