Depth perception from blurring-a neural networks based approach for automated visual inspection in VLSI wafer probing

An approach to the determination of depth as a function of blurring for automated visual inspection in VLSI wafer probing is presented. There exists a smooth relationship between the degree of blur and the distance of a problem from a test pad on a VLSI chip. Therefore, by measuring the amount of blurring, the distance from contact can be estimated. The effect of blurring on a point-object is studied in the frequency domain, and a monolithic relationship is found between the degree of blur and the frequency content of the image. Fourier feature extraction, with its inherent property of shift-invariance, was utilized to extract significant feature vectors. These vectors contain information on the degree of blur, and hence the distance from the probe. Neural networks were employed to map these feature vectors onto the actual distances. The network was then used in the recall mode to linearly interpolate the distance corresponding to the significant Fourier features of a blurred image