Title :
The design and training of an intelligent sensor implementation
Author :
Yu, Xiangui ; Loh, Nan K. ; Jullien, G.A. ; Miller, W.C.
Author_Institution :
Dept. of Electr. Eng., Windsor Univ., Ont., Canada
Abstract :
The design and VLSI implementation of an intelligent sensor that can be used for process control applications requiring image capture or non-contact measurement is presented in this paper. The sensor architecture is based on an analog VLSI realization of an artificial neural network with an integrated photosensitive array. A 10×10 array of photosensitive cells has been designed as the input nodes to a two-layer feedforward neural network. Four groups each with 25 photosensors are fully connected to 4 neurons respectively in the hidden layer. The 16 hidden neurons are fully connected to 5 neurons in the output layer. A development program has been written that determines the optimal set of weights using the modified backpropagation algorithm. The neural network has been trained to recognize 18 different types of patterns. The final implementation of this intelligent sensor has the dimensions of 4500×4820 design scale microns and contains approximately 1630 analog devices fabricated using a 3μ single-polysilicon, double-metal, p-well CMOS process
Keywords :
CMOS integrated circuits; VLSI; backpropagation; computer vision; feedforward neural nets; image processing equipment; image sensors; intelligent sensors; photodetectors; process computer control; 3 mum; Si; VLSI; analog VLSI realization; artificial neural network; design; development program; double-metal p-well CMOS process; hidden layer; image capture; integrated photosensitive array; intelligent sensor; intelligent sensor implementation; modified backpropagation algorithm; noncontact measurement; photosensitive cells; photosensors; polysilicon; process control applications; sensor architecture; training; two-layer feedforward neural network; Artificial neural networks; Backpropagation algorithms; Feedforward neural networks; Intelligent sensors; Neural networks; Neurons; Pattern recognition; Process control; Sensor arrays; Very large scale integration;
Conference_Titel :
Electrical and Computer Engineering, 1993. Canadian Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-2416-1
DOI :
10.1109/CCECE.1993.332465