Predicting frequency deviation of a crystal oscillator based on long short-term memory network and transfer learning technique

Bo Chen Su, Duc Huy Nguyen, Paul C.P. Chao*

*此作品的通信作者

研究成果: Article同行評審

摘要

Crystal oscillators are fundamental to an extensive range of electronic systems, spanning computers, mobile phones, and automotive electronics. Their significance is accentuated in high-precision applications such as global positioning systems (GPS) and aerospace systems where the frequency-temperature characteristics and thermal hysteresis phenomena are of paramount importance. This study introduces a groundbreaking approach for predicting frequency deviations arising from thermal hysteresis using Long Short-Term Memory (LSTM) networks. Contrary to prior research which predominantly utilized cubic functions to model frequency-temperature characteristics and frequently overlooked thermal hysteresis, this investigation distinguishes itself by leveraging LSTM. The proposed methodology is aptly designed to model both time-dependent and temperature-dependent variations, consequently offering a heightened precision in predicting frequency deviations. By integrating transfer learning techniques, the model's adaptability to diverse databases is augmented, broadening its utility. Experimental evaluations with real-world data underscore the preeminence of the introduced method, registering a root mean square error (RMSE) of less than 0.05 ppm, more favorable than that by the traditional cubic functions and all the prior arts.

原文English
期刊Microsystem Technologies
DOIs
出版狀態Accepted/In press - 2024

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