Reservoir computing (RC) decomposes the recurrent neural network into a fixed network with recursive connections and a trainable linear network. With the advantages of low training cost and easy hardware implementation, it provides a method for the effective processing of time-domain correlation information. In this paper, we build a hardware RC system with a nonlinear MEMS resonator and build an action recognition data set with time-domain correlation. Moreover, two different universal data set are utilized to verify the classification and prediction performance of the RC hardware system. At the same time, the feasibility of the novel data set was validated by three general machine learning approaches. Specifically, the processing of this novel time-domain correlation data set obtained a relatively high success rate. These results, together with the dataset that we build, enable the broad implementation of brain-inspired computing with microfabricated devices, and shed light on the potential for the realization of integrated perception and calculation in our future work.

High-sensitivity MEMS magnetoresistive (MR) sensors have attracted ever-increasing attention due to their ability to detect weak magnetic fields, but the resolution is severely limited by the $1/f$ noise. This paper reports a MEMS MR sensor that can effectively suppress the $1/f$ noise by modulating the magnetic field signal to the higher frequency region utilizing magnetic tunnel junction vertical motion modulation based on the MEMS piezoelectric cantilever resonator. The magnetic sensitivity can be increased to 2283.3%/mT with a high magnetic gain of 39.3 by integrating the structure-optimized fixed magnetic flux concentrators. The signal-to-noise ratio of the MEMS MR sensor can be improved by three orders of magnitude and the 160 pT/√Hz resolution can be reached. These results make the MEMS MR sensor based on the MTJ motion modulation a competitive candidate in the pico-Tesla magnetic detection.

The piezoelectric cantilever resonator is used widely in many fields because of its perfect design, easy-to-control process, easy integration with the integrated circuit. The tip displacement and resonance frequency are two important characters of the piezoelectric cantilever resonator and many models are used to characterize them. However, these models are only suitable for the piezoelectric cantilever with the same width layers. To accurately characterize the piezoelectric cantilever resonators with different width layers, a novel model is proposed for predicting the tip displacement and resonance frequency. The results show that the model is in good agreement with the finite element method (FEM) simulation and experiment measurements, the tip displacement error is no more than 6%, the errors of the first, second, and third-order resonance frequency between theoretical values and measured results are 1.63%, 1.18%, and 0.51%, respectively. Finally, a discussion of the tip displacement of the piezoelectric cantilever resonator when the second layer is null, electrode, or silicon oxide (SiO₂) is presented, and the utility of the model as a design tool for specifying the tip displacement and resonance frequency is demonstrated. Furthermore, this model can also be extended to characterize the piezoelectric cantilever with n-layer film or piezoelectric doubly clamped beam.

A mode-localized ΔE-effect magnetic sensor model is established theoretically and numerically. Based on the designed weakly coupled resonators with multi-layer film structure, it is investigated how the ΔE-effect of the magnetostrictive film under the external magnetic field causes the stiffness perturbation of the coupled resonators to induce the mode localization effect. Using the amplitude ratio (AR) as the output in the mode-localized ΔE-effect magnetic sensor can improve the relative sensitivity by three orders of magnitude compared with the traditional frequency output, which has been verified by simulations based on the finite element method (FEM). In addition, the effects of material properties and geometric dimensions on sensor performance parameters, such as sensitivity, linear range, and static operating point are also analyzed and studied in detail, providing the theoretical basis for the design and optimization of the mode-localized ΔE-effect magnetic sensor in different application scenarios. By reasonably optimizing the key parameters of the weekly coupled resonators, a mode-localized ΔE-effect magnetic sensor with the sensitivity of 18 AR/mT and a linear range of 0.8 mT can be achieved.

Microelectromechanical system (MEMS) resonators have been widely used in the magneto-resistive (MR) sensor for modulating the magnetic flux to enhance the detection limit. However, the manufacturing tolerances in MEMS fabrication processes make it challenging to fabricate the identical resonators with the same vibration frequency, which greatly decreases the detection limit of the MR sensor. To synchronize the MEMS resonators and improve the performance of the MR sensor, the double-ended tuning fork (DETF) based comb-driven MEMS resonators is proposed in this paper, making the system operate at the out-of-phase mode to complete the synchronization. The dynamic behaviour of the resonators is investigated through theoretical analysis, numerical solution based on MATLAB code and Simulink, and experimental verification. The results show that the transverse capacitances in the comb will significantly affect the resonance frequency due to the second-order electrostatic spring constant. It is the first time to observe the phenomenon that the resonant frequency increases with the increase of the bias, and it can also decrease with increasing the bias through adjusting the initial space between the fixed finger and the moving mass, they are different from the model about spring softening and spring hardening. Besides, the proposed DETF based comb-driven resonators can suppress the in-phase and out-of-phase mode through adjusting the driving and sensing ports, and sensing method, meanwhile make the magnetic flux modulation fully synchronized, and maximize the modulation efficiency, and minimize the detection limit. These characteristics are appropriate for the MR sensor, even other devices that need to adjust the resonance frequency and vibration amplitude. Furthermore, the model and the design can also be extended to characteristic the single end tuning fork based MEMS resonator and other MEMS-based MR sensors.

MEMS-based magnetoresistive (MR) sensors have attracted considerable attention for years due to their small weight, low power consumption, and high sensitivity, whereas the 1/ $f$ noise is larger in the low-frequency region and degrades the low-frequency magnetic field detection limit. To reduce the impact of 1/ $f$ noise, MEMS resonators are utilized to modulate the low-frequency field to a high-frequency region where the 1/ $f$ noise vanishes. This article proposes a novel modulation method called synchronous motion modulation (SMM) using a comb-driven resonator and a piezoelectric cantilever resonator; meanwhile, it analyzes the relationship of modulation efficiency of SMM, vertical motion modulation (VMM), and gap change modulation (GCM) theoretically and with finite element method (FEM) simulation. It is found that the theoretical analysis is coincident with numerical simulation, and the SMM is the optimal modulation method for improving the low-frequency magnetic field detection limit due to its modulation efficiency is the sum of that of VMM and GCM.