An acetone microsensor with a ring oscillator circuit fabricated using the commercial 0.18 μm CMOS process

This study investigates the fabrication and characterization of an acetone microsensor with a ring oscillator circuit using the commercial 0.18 μm complementary metal oxide semiconductor (CMOS) process. The acetone microsensor contains a sensitive material, interdigitated electrodes and a polysilicon heater. The sensitive material is α-Fe2O3 synthesized by the hydrothermal method. The sensor requires a post-process to remove the sacrificial oxide layer between the interdigitated electrodes and to coat the α-Fe2O3 on the electrodes. When the sensitive material adsorbs acetone vapor, the sensor produces a change in capacitance. The ring oscillator circuit converts the capacitance of the sensor into the oscillation frequency output. The experimental results show that the output frequency of the acetone sensor changes from 128 to 100 MHz as the acetone concentration increases 1 to 70 ppm

Manufacture and Characterization of High Q-Factor Inductors Based on CMOS-MEMS Techniques

A high Q-factor (quality-factor) spiral inductor fabricated by the CMOS (complementary metal oxide semiconductor) process and a post-process was investigated. The spiral inductor is manufactured on a silicon substrate. A post-process is used to remove the underlying silicon substrate in order to reduce the substrate loss and to enhance the Q-factor of the inductor. The post-process adopts RIE (reactive ion etching) to etch the sacrificial oxide layer, and then TMAH (tetramethylammonium hydroxide) is employed to remove the silicon substrate for obtaining the suspended spiral inductor. The advantage of this post-processing method is its compatibility with the CMOS process. The performance of the spiral inductor is measured by an Agilent 8510C network analyzer and a Cascade probe station. Experimental results show that the Q-factor and inductance of the spiral inductor are 15 at 15 GHz and 1.8 nH at 1 GHz, respectively

LSTM RNN-based excitation force prediction for the real-time control of wave energy converters

Wave energy is a type of abundant and dense renewable energy. Wave force prediction is a critical technology that influences power absorption efficiency in the real-time control of wave energy converter (WEC). Could wave elevation be used to predict wave excitation force directly by training artificial neural network? This method results in rapid and suitable prediction for real-time control. A long short-term memory recurrent neural network (LSTM RNN) algorithm is introduced to identify characteristics of wave excitation forces based on wave elevations. In this method, the wave elevations in front of the structure are measured to obtain sufficient time to actuate the control manipulation. A total of 180 regular wave and 12 irregular wave tests are conducted, and the LSTM RNN model is trained based on the experimental results. The performance of the LSTM algorithm is verified. According to the regular cases in the study, the LSTM prediction can identify high-order harmonic loads, and the anti-noise capability of the LSTM algorithm can filter random noises from the measure signals. In the irregular cases, the LSTM RNN algorithm performs effectively to predict the wave force excited on the structure using wave elevations measured by wave probes. The best combinations of the test setting parameters are determined to guide experimental tests and WEC prototypes