An Efficient Earth Magnetic Field MEMS Sensor: Modeling, Experimental Results, and Optimization

Abstract

HIS PAPER gives a complete description of the work briefly presented in [1]. Consumer products have recently started incorporating micro-magnetometers to be used as compasses. In combination with pressure sensors, accelerometers and gyroscopes, these sensors allow improved navigation both in scenarios where GPS is available and when it is not, such as for indoor navigation. In some cases this trend has already suggested the commercialization of multi-axis (or combo) MEMS sensor units.Lorentz force MEMS magnetometers [2]-[9] can be particularly advantageous with respect to other devices for the measurement of magnetic fields in the direction orthogonal to the substrate (z-axis magnetometers). When compared to Hall-effect sensors, Lorentz force devices have the advantage of lower power consumption and easier integration with standard, silicon-based, MEMS fabrication technologies.

Stochastic Effects on the Dynamics of the Resonant Structure of a Lorentz Force MEMS Magnetometer

“…Stochastic effects on the device sensitivity, which results to be proportional to the amplitude V max of the oscillations of the resonant structure, are now assessed. These effects play a role in defining confidence intervals for additional performance indices like, e.g., power consumption, bandwidth, and resolution, see [5][6][7]. The focus of this study is anyway on micro-scale imperfections, as measured by the scattering in the beam width h and in the effective Young's modulus E of the polysilicon film around the target values.…”

“…Once more, this is a proof of the much smaller impact of on the solution and, therefore, on the device performance, in comparison to that of . To quantify next the sensitivity of the magnetometer, we refer to the differential sensing scheme provided by the two parallel plate capacitors depicted in Figure 1, see [4,7]. The magnitude ∆C of the capacitance change due to the deformation of the beam, or flexure is given by:…”

“…To design and predict the performance of these micro-devices, accurate models are required to account for the interaction among various physical fields, primarily the thermal, electric, magnetic, and mechanical ones. Numerical and analytical methods have been recently proposed to understand the underlying working principles, and increase the capability to match experimental evidences [4][5][6][7].…”

“…Indeed, sensitivity is not the only performance index to account for in the design, since power consumption is of paramount importance for mobile applications. In this regard, a so-called balanced optimization scheme was proposed in [6,7] to allow also for bandwidth and resolution of a capacitive, single-axis Lorentz force magnetometer similar to that here considered, see also [23][24][25].…”

Stochastic Mechanical Characterization of Polysilicon MEMS: A Deep Learning Approach

“…For polysilicon MEMS, the effects of the crystalline morphology on the reliability of inertial devices subjected to impacts and also under operational conditions were studied by the authors in [8][9][10][11][12][13][14][15][16] to possibly drive their optimization. To characterize the micro-devices on the basis of real experimental data, an on-chip test procedure was also proposed and analyzed in [17][18][19][20].…”