TY - JOUR
T1 - Predicting frequency deviation of a crystal oscillator based on long short-term memory network and transfer learning technique
AU - Su, Bo Chen
AU - Nguyen, Duc Huy
AU - Chao, Paul C.P.
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85194842001&partnerID=8YFLogxK
U2 - 10.1007/s00542-024-05691-2
DO - 10.1007/s00542-024-05691-2
M3 - Article
AN - SCOPUS:85194842001
SN - 0946-7076
JO - Microsystem Technologies
JF - Microsystem Technologies
ER -