Method and device for adjusting equivalent Q value of silicon microresonator RC (Resistance-Capacitance) system by mechanical pumping

Abstract

The invention relates to a method and a device for adjusting an equivalent Q value of a silicon microresonator RC (Resistance-Capacitance) system by utilizing mechanical pumping. The method comprises the following steps of: determining a resonator modal frequency and a mechanical pumping signal; the equivalent Q value is dynamically adjusted through mechanical pumping to verify the experiment; further performing complex nonlinear response on the pre-processed to-be-tested signal by using a coupling MEMS resonator RC system under specified conditions according to a verification experiment; filtering and collecting a response signal of the resonator; accurate control, data detection, downsampling, logical operation and the like of a digital delay feedback control program; and training and testing the test data set. According to the method, energy exchange between different modes of the resonator is achieved through mechanical pumping, and dynamic adjustment of the equivalent Q value of the used modes is achieved under the condition that it is guaranteed that the system works under the strong non-linear rich condition (the high vacuum environment). According to the method, the nonlinear mapping performance of reservoir calculation is ensured, and the calculation speed is increased.

Description

technical field [0001] The disclosure relates to the field of neural network computing, and is characterized in that it relates to a method and a device for dynamically adjusting the equivalent Q value of a silicon microresonator RC system by using a mechanical pump. Background technique [0002] Reservoir Computing (RC for short) is a neural network algorithm model improved on the basis of Recurrent Neural Network (RNN for short). It usually consists of an input layer and a large number of randomly fixed-connected nonlinear nodes. Reservoir pool and the output layer. Its input connection weights and internal connection weights of the reserve pool are randomly generated and remain unchanged, and only the output connection weights need to be trained. RC has been widely used in many scenarios due to its simple training method and excellent performance in classification tasks such as time series signal prediction and speech recognition, especially driving the research boom in ...

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Problems solved by technology

However, this method does not optimize the design of the resonator system, the selection of nonlinear operating points, etc., resulting in excessive power consumption of the circuit in practical applications, and the classification accuracy is low. The test accuracy of the TI-46 isolated speech digital data set The best value is 78±2%, which cannot meet more complex tasks; and according to different types of prediction tasks, the overall parameters can only be adjusted by recalibrating the nonlinear operating point, which cannot meet the needs of actual sensor-level application scenarios as a laboratory research protocol only

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