Electrowetting Enhanced Analog Self-Powered Touch Panel with Deep Learning Enabled Digit Recognition
SSRN, ISSN: 1556-5068
2024
- 62Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Human-machine interfaces, with contact positioning as a critical functionality, are driving the development of next-generation intelligent wearable electronics. Despite the energy-saving advantages of self-powered triboelectric touch panels, their widespread application is hindered by challenges such as poor signal consistency, limited long-term signal strength enhancement methods, and the complexity of managing numerous signal channels. In this study, we propose an analog triboelectric contact positioning method that significantly reduces signal channel requirements and eliminates amplitude dependence by using a resistance bar design to correlate the touch position with the voltage ratio across just four signal channels. Additionally, a fluorinated acrylate-polyethylene terephthalate double-layer structure is introduced to facilitate electrowetting-assisted charge injection, enabling efficient charge storage due to the enhanced charge penetration characteristics. Our results show that the triboelectric performance decreases by only 26% after droplet wetting and by 15% after 5000 contact-separation cycles, demonstrating excellent wetting resistance and long-term stability. Furthermore, we apply deep learning techniques to process data from the four-channel touch panel, enabling handwritten digit recognition with an accuracy of 98.5% among five digits through cross-channel feature extraction and comparison, and offering insights into the integration of artificial intelligence in triboelectric signal processing.
Bibliographic Details
Elsevier BV
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