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

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.