Machine Learning Empowered Thin Film Acoustic Wave Sensing

Tan, Kaitao, Ji, Zhangbin, Zhou, Jian, Deng, Zijing, Zhang, Songsong, Gu, Yuandong, Guo, Yihao, Zhuo, Fengling, Duan, Huigao and Fu, Yong Qing (2023) Machine Learning Empowered Thin Film Acoustic Wave Sensing. Applied Physics Letters, 122 (1). 014101. ISSN 0003-6951

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Thin film based surface acoustic wave (SAW) technology has been extensively explored for physical, chemical and biological sensors. However, these sensors often show inferior performance for a specific sensing in complex environments, as they are affected by multiple influencing parameters and their coupling interferences. To solve these critical issues, we propose a methodology to extract critical information from the scattering parameter and combine machine learning method to achieve multi-parameter decoupling. We used AlScN film-based SAW device as an example, in which highly c-axis orientated and low stress AlScN film was deposited on silicon substrate. The AlScN/Si SAW device showed a Bode quality factor value of 228 and an electro-mechanical coupling coefficient of ~2.3. Two sensing parameters (i.e., ultraviolet or UV and temperature) were chosen for demonstration and the proposed machine-learning method was used to distinguish their influences. Highly precision UV sensing and temperature sensing were independently achieved without their mutual interferences. This work provides an effective solution for decoupling of multi-parameter influences and achieving anti-interference effects in thin film based SAW sensing.

Item Type: Article
Additional Information: Funding information: This work was supported by the NSFC (No.52075162), The Program of New and Hightech Industry of Hunan Province (2020GK2015, 2021GK4014), The Joint Fund Project of the Ministry of Education, The Excellent Youth Fund of Hunan Province (2021JJ20018), the Key Research & Development Program of Guangdong Province (2020B0101040002), International Exchange Grant (IEC/NSFC/201078) through Royal Society UK and the NSFC.
Subjects: H100 General Engineering
H800 Chemical, Process and Energy Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Rachel Branson
Date Deposited: 03 Jan 2023 10:23
Last Modified: 17 Jan 2023 09:00

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