On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing

An intelligent sensor for light wavelength readout, suitable for visible range optical applications, has been developed. Using buried triple photo-junction as basic pixel sensing element in combination with artificial neural network (ANN), the wavelength readout with a full-scale error of less than 1.5% over the range of 400 to 780 nm can be achieved. Through this work, the applicability of the ANN approach in optical sensing is investigated and compared with conventional methods, and a good compromise between accuracy and the possibility for on-chip implementation was thus found. Indeed, this technique can serve different purposes and may replace conventional methods.

[1]  Adriaan van den Bos,et al.  An ANN-based smart capacitive pressure sensor in dynamic environment , 2000 .

[2]  Jenq-Neng Hwang,et al.  Handbook of Neural Network Signal Processing , 2000, IEEE Transactions on Neural Networks.

[3]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[4]  L. Saidi,et al.  Modeling of a Capacitive Pressure Sensor using Artificial Neural Networks , 2006, 2006 2nd International Conference on Information & Communication Technologies.

[5]  P.L.P. Dillon,et al.  Fabrication and performance of color filter arrays for solid-state imagers , 1978, IEEE Transactions on Electron Devices.

[6]  Pasquale Daponte,et al.  ANN-based error reduction for experimentally modeled sensors , 2002, IEEE Trans. Instrum. Meas..

[7]  S. Sangwine,et al.  The Colour Image Processing Handbook , 1998, Springer US.

[8]  O. Postolache,et al.  Fitting transducer characteristics to measured data , 2001 .

[9]  P.L.P. Dillon,et al.  Fabrication and performance of color filter arrays for solid-state imagers , 1978 .

[10]  José Rivera,et al.  Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks , 2007, Sensors (Basel, Switzerland).

[11]  Pasquale Daponte,et al.  Artificial neural networks in measurements , 1998 .

[12]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[13]  Octavian Postolache,et al.  A temperature-compensated system for magnetic field measurements based on artificial neural networks , 1998, IEEE Trans. Instrum. Meas..

[14]  Mohamed Ben Chouikha,et al.  Modeling and design of multiple buried junctions detectors for color systems development , 2000, Design, Test, Integration, and Packaging of MEMS/MOEMS.

[15]  G N Lu.  A dual-wavelength method using the BDJ detector and its application to iron concentration measurement , 1999 .

[16]  Jagdish Chandra Patra,et al.  Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment , 2005, EURASIP J. Adv. Signal Process..

[17]  Gui Yun Tian,et al.  Design and implementation of distributed measurement systems using fieldbus-based intelligent sensors , 2001, IEEE Trans. Instrum. Meas..

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

[19]  Salvatore Graziani,et al.  Ultrasonic distance sensor improvement using a two-level neural network , 1995, Proceedings of 1995 IEEE Instrumentation and Measurement Technology Conference - IMTC '95.

[20]  G. Sou,et al.  Investigation of CMOS BDJ detector for fluorescence detection in microarray analysis , 2000, 1st Annual International IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine and Biology. Proceedings (Cat. No.00EX451).

[21]  Gerard Sou,et al.  COLOUR DETECTION USING BURIED TRIPLE PN JUNCTION STRUCTURE IMPLEMENTED IN BICMOS PROCESS , 1998 .