RESUMEN
To ensure that surface acoustic wave (SAW) filters fulfill the requirements of Carrier Aggregation (CA) applications, the development of modeling tools that can forecast and simulate high-frequency spurious responses has been necessary. This paper presents an advanced methodology for extending the coupling-of-modes (COM) model to obtain precise modeling of the high-frequency spurious responses of incredible high-performance surface acoustic wave (I.H.P. SAW) devices. The extended COM (ECOM) model is derived by modifying the conventional COM model and extending it accordingly. The parameters used in this model are determined through numerical fitting. For validation, firstly, the ECOM model is applied to a one-port synchronous I.H.P. SAW resonator, and the simulation and measurement results match. Then, the structural parameters of the ECOM model are varied, and the accuracy of the model after the structural parameters are varied is verified. It is demonstrated that this model can be applied to the design work of SAW filters. Finally, the ECOM model is applied to the design of the I.H.P. SAW filter based on a 42°YX-LiTaO3 (LT)/SiO2/AlN/Si structure. By using this method, the I.H.P. SAW filter's high-frequency spurious response can be predicted more accurately.
RESUMEN
To efficiently process the massive amount of sensor data, it is demanding to develop a new paradigm. Inspired by neurobiological systems, an infrared near-senor reservoir computing (RC) system, consisting of infrared sensors and memristors based on single-crystalline LiTaO3 and LiNbO3 (LN) thin film respectively, is demonstrated. The analog memristor is used as a reservoir in the RC system to process sensor signals with spatiotemporal characteristics. LN crystal structure stacked with oxygen octahedra provides favorable conditions for reliable Mott variable-range hopping conduction, which provides the memristor with tens of thousands of reservoir states within a large dynamic range. With the characteristics, the analog sensor signals with high data fidelity can be directly fed to the memristive reservoir, and the spatiotemporal features can be separated and mapped. The system demonstrated a dynamic gesture perception task, achieving an accuracy of 99.6%, which highlights the great application potential of the memristor in signal sensor processing and will advance the application of artificial intelligence in sensor systems. Crystal ion slicing techniques are used to fabricate a single-crystalline thin film for both the memristor and sensor, which opens up the possibility of realizing monolithic integration of a memristor-based near-sensor computing system.