Intelligent sensors using neural networks: the example of a microsystem for visual inspection

The aim of this article is to show how artificial neural networks and 3D packaging technology have a major role to play in the development of microsystems. A visual inspection system for real-time identification of objects in a scene is described. The system comprises a CMOS or CCD imager, an analogue preprocessing stage that includes a learning mechanism for adapting the system to images of different contrast, and a neural classification stage. The detection of a matrix code using as the classifier a vector support machine is illustrated. As the latter is difficult to realise in VLSI the author has turned to the threshold neural network `Offset´, which constructs a parity machine, i.e. a network comprising a single layer of neurons, the output being obtained with the help of a simple exclusive-OR logic gate. Unfortunately the parity machine suffers from overtraining, as the OffSet algorithm converges to a zero error over the entire training base. Nevertheless, if good implementation strategies are available, it is possible to improve the performance in general by combining a large number of classifiers by majority voting. A CMOS VLSI circuit, called SysNeuro, has been fabricated which integrates a parity machine in a square systolic architecture of 4×4 processors. This circuit has variable precision. The number of neurons has been increased by combining 4 SysNeuro chips in a multichip module and stacking three of the modules to form a 3D structure-SysNeuro3D