Wafer Defect Map Classification Using Sparse Convolutional Networks

Chips in semiconductor manufacturing are produced in circular wafers that are constantly monitored by inspection machines. These machines produce a wafer defect map, namely a list of defect locations which corresponds to a very large, sparse and binary image. While in these production processes it is normal to see defects that are randomly spread through the wafer, specific defect patterns might indicate problems in the production that have to be promptly identified.