High precision of sign language recognition based on In2O3transistors gated by AlLiO solid electrolyte.
Nanotechnology
; 35(8)2023 Dec 08.
Article
em En
| MEDLINE
| ID: mdl-37995377
ABSTRACT
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.
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MEDLINE
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En
Ano de publicação:
2023
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Article