近年来,晶体管的突触特性已被广泛研究。与基于液体或有机材料的晶体管相比，无机固体电解质栅极晶体管具有化学稳定性好的优点。这项研究使用了一个简单的，AlLiO固体电解质制备In2O3晶体管的低成本溶液技术。该器件的电化学性能是通过形成双电层和电化学掺杂来实现的，可以模仿生物突触的基本功能，如兴奋性突触后电流(EPSC)，成对脉冲促进(PPF)，和尖峰时间依赖性可塑性(STDP)。此外，成功模拟了复杂的突触行为，例如巴甫洛夫经典条件和摩尔斯电码“青岛”。识别准确率达95%,建立了基于晶体管的人工神经网络来识别手语并实现手语解释。此外,手写数字的识别准确率为94%。即使有各种级别的高斯噪声，识别率仍在84%以上。上述发现证明了In2O3/AlLiOTFT在塑造下一代人工智能方面的潜力。
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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 In2O3transistors 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, paired-pulse promotion, and spiking time-dependent plasticity. Furthermore, complex synaptic behaviors such as Pavlovian classical conditioning is 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.
