Analog ASIC for improved temperature drift compensation of a high sensitive porous silicon pressure sensor

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 μm AMS technology model and simulated using mentor graphics ELDO Simulator.

[1]  L. MacEachern,et al.  A precision CMOS analog cubing circuit , 2004, The 2nd Annual IEEE Northeast Workshop on Circuits and Systems, 2004. NEWCAS 2004..

[2]  R. FitzHugh.  Impulses and Physiological States in Theoretical Models of Nerve Membrane. , 1961, Biophysical journal.

[3]  Henry Guckel,et al.  Surface micromachined pressure transducers , 1991 .

[4]  Daniel Roviras,et al.  Functional tests of a 0.6 μm CMOS MLP analog neural network for fast on-board signal processing , 2008 .

[5]  Hiranmay Saha,et al.  Temperature compensation of piezoresistive micro-machined porous silicon pressure sensor by ANN , 2006, Microelectron. Reliab..

[6]  J. Ramirez-Angulo,et al.  A compact high frequency VLSI differential analog adder , 1996, Proceedings of the 39th Midwest Symposium on Circuits and Systems.

[7]  Robert G. Meyer,et al.  Analysis and Design of Analog Integrated Circuits , 1993 .

[8]  Nicolás J. Medrano-Marqués,et al.  A general method for sensor linearization based on neural networks , 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).

[9]  Susumu Kuroyanagi,et al.  Fabrication of microdiaphragm pressure sensor utilizing micromachining , 1992 .

[10]  Hiranmay Saha,et al.  Modeling, simulation and temperature compensation of porous polysilicon capacitive humidity sensor using ANN technique , 2005, Microelectron. Reliab..

[11]  Cheng-Chieh Chang,et al.  Low-Voltage Analog Tripler Circuit , 2001 .

[12]  A. C. Neumann.  Slow domains in semi-insulating GaAs , 2001 .

[13]  H. Saha,et al.  Low Pressure Piezoresistive Sensors for Medical Electronics Applications , 2006 .

[14]  Yoshihiro HAYAKAWA,et al.  Characteristics of Inverse Delayed Model for Neural Computation , 2002 .

[15]  Ganapati Panda,et al.  An intelligent pressure sensor using neural networks , 2000, IEEE Trans. Instrum. Meas..

[16]  C. Pramanik,et al.  Piezoresistive pressure sensing by porous silicon membrane , 2006, IEEE Sensors Journal.

[17]  Hiranmay Saha,et al.  An integrated pressure and temperature sensor based on nanocrystalline porous silicon , 2006 .