The paper focuses on the design of a CMOS analog ASIC for temperature-drift compensation of a high sensitivity piezoresistive micro-machined porous silicon pressure sensor to avoid analog-to-digital conversion, limit chip area and reduce power consumption. For implementing the compensation circuitry, multilayered perceptron (MLP) based artificial neural network (ANN) with inverse delayed function model of neuron has been optimized. The temperature drift compensation CMOS ASIC has been implemented to make porous silicon pressure sensor an excellent SMART porous silicon pressure sensor. Using the compensation circuit, the error in temperature-drift has been minimized from 93% to about 0.5% as compared to 3% using conventional neuron model in the temperature range of 25-80°C. The entire circuit has been designed using 0.35 lm AMS technology model and simulated using mentor graphics ELDO Simulator.
Abstract:In this paper a nanocrystalline (nc) zinc oxide based hybrid gas sensor with signal conditioning ASIC has been reported for sensing and transmitting the information about methane concentration from the underground coalmine environment. A low power, low temperature nc zinc oxide MEMS based gas sensor has been designed, fabricated and tested for the purpose with a power consumption of ~70mW and sensitivity of 76.6 % at 1.0% methane concentration at a sensor operating temperature of 150 0 C. For transmitting the output of the gas sensor, a voltage controlled oscillator (VCO) chip integrated with a low noise amplifier has been fabricated in 0.35µm CMOS technology to convert the voltage output of the gas sensor to desirable frequency. The power consumption of the chip has been obtained to be around 3mW. The amplifier gain is set suitably ~13 to apply the desirable control voltage (~1.2V-3.2V)to the VCO. The noise of the amplifier has been obtained to be around 2µV/Hz 1/2 . The output frequency of the VCO varies from 20kHz to 100kHz for the change in methane concentration from 0 to 1%. The output of the VCO chip can be applied as a modulating signal to a commercially available transceiver, which transmits the signal to the control room.
Electrochemical micro-electro mechanical systems (MEMS) seismic sensor is limited by its nonideality of frequency dependent characteristics hence interface circuits for compensation is necessary. The conventional compensation circuits are limited by high power consumption, bulky external hardware. These digital circuits are limited by inherent analog to digital conversions which consumes significant power, acquires more size and limits processing speed. This system presents field programmable analog array (FPAA) (Anadigm AN231E04) based hardware implementation of artificial neural network (ANN) model with minimized error in frequency drift in the range of 3.68% to about 0.64% as compared to ANN simulated results in the range of 23.07% to 0.99%. Single neuron consumes power of 206.62 mW with minimum block wise resource utilization. The proposed hardware uses all analog blocks removing the requirement of analog to digital converter and digital to analog converter, reducing significant power and size of interface circuit and enhancing processing speed. It gives the reliable, SMART MEMS seismic sensor with ANN-based intelligent interface circuit implemented in FPAA hardware.
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