Magnetic field sensors are becoming an essential part of everyday life due to the improvements in their sensitivities and resolutions, while at the same time they have become compact, smaller in size and economical. In the work presented herein a Lorentz force based CMOS-MEMS magnetic field sensor is designed, fabricated and optically characterized. The sensor is fabricated by using CMOS thin layers and dry post micromachining is used to release the device structure and finally the sensor chip is packaged in DIP. The sensor consists of a shuttle which is designed to resonate in the lateral direction (first mode of resonance). In the presence of an external magnetic field, the Lorentz force actuates the shuttle in the lateral direction and the amplitude of resonance is measured using an optical method. The differential change in the amplitude of the resonating shuttle shows the strength of the external magnetic field. The resonance frequency of the shuttle is determined to be 8164 Hz experimentally and from the resonance curve, the quality factor and damping ratio are obtained. In an open environment, the quality factor and damping ratio are found to be 51.34 and 0.00973 respectively. The sensitivity of the sensor is determined in static mode to be 0.034 µm/mT when a current of 10 mA passes through the shuttle, while it is found to be higher at resonance with a value of 1.35 µm/mT at 8 mA current. Finally, the resolution of the sensor is found to be 370.37 µT.
Functional neuroimaging modalities play an important role in deciding the diagnosis and course of treatment of neuronal dysfunction and degeneration. This article presents an analytical tool with visualization by exploiting the strengths of the MEG (magnetoencephalographic) neuroimaging technique. The tool automates MEG data import (in tSSS format), channel information extraction, time/frequency decomposition, and circular graph visualization (connectogram) for simple result inspection. For advanced users, the tool also provides magnitude squared coherence (MSC) values allowing personalized threshold levels, and the computation of default model from MEG data of control population. Default model obtained from healthy population data serves as a useful benchmark to diagnose and monitor neuronal recovery during treatment. The proposed tool further provides optional labels with international 10-10 system nomenclature in order to facilitate comparison studies with EEG (electroencephalography) sensor space. Potential applications in epilepsy and traumatic brain injury studies are also discussed.