This article documents the design, manufacture, and testing of a silicon inertial optical sensor for low-frequency (lower than 2 kHz) applications. Three accelerometer designs optimized by parameterization using Finite Element Analysis were considered. The accelerometers were manufactured and the one with the highest performance at low frequency was chosen for testing, which was attached to a steel package. The feasibility of using probes, based on micro-machined sensing elements, to measure mechanical vibrations with high resolution was also studied. The detection is performed with an air interferometer, eliminating the need for electric signals that are susceptible to electromagnetic interference and large temperature variations. From the fabrication technology using only a silicon wafer with both sides etched, the frequency response of the sensor, temperature operation (higher than 85 °C) and with a resolution of 17.5 nm, it was concluded that is achievable and feasible to design and manufacture an optical vibration sensor for potential harsh environments with a low cost.

This paper presents a proposal for parameter extraction of a resistive load inverter circuit, with a Thin Film Transistor (TFT), using Artificial Neural Networks, Random Forest, Decision Trees and Support Vector Regression. Although analytical and optimization methods are usually used for this purpose, they have disadvantages such as the need for expertise or complex implementation. This work shows that these supervised learning methods are useful for this task because they can learn the parameters of the device transfer curves, obtaining a good fit between the measurements and the extracted parameters. The different methods were trained using a data set constructed from simulations performed with AIM-Spice software, where the parameters affecting different regions of the inverter characteristic curve were extracted. In the experimental stage, the Neural Networks obtained better results, with an average error rate of 6.04%. The method was also applied to real NMOS measurements and yielded minimum errors of up to 0.43%.

The present work describes a theoretical design of a micro electro thermal resonator based on a tuning fork that can be tuned by design at ultrasound frequencies, from 30 kHz up to 200 kHz. The device is proposed on PolyMUMPS process where the minimum feature dimension is four micro meters. This design is aimed as a platform for physical or chemical sensors, operated with lower voltage than electrostatic tuning forks. The proposal is validated by a finite element analysis performed in COMSOL Multiphysics, the results show that electro thermal actuation is an alternative strategy for resonator designs without temperature restrictions with a power dissipation of 29 mW and operation voltage lower than 20 V.