Inspecting and classifying physical failures in MEMS substrates during fabrication using computer vision

Abstract

Visual inspection for defects has been predominantly used in all realms of the manufacturing sector. MEMS substrates and circuits face multiple challenges during the fabrication phase due to the highly reduced dimensions and the intricacy of structures developed over the substrate. The key to obtaining smooth microstructure manufacturing is to ensure that the substrate is flawless i.e., without physical defects. The manufacturing process of the metallic wafers does include a quality assessment section, however, that involves a simple dye-based comparison and rejection of the chip in case there is a defect detected in the chip. In the current manufacturing scenario, the defects are not analyzed further than this considering the complexity involved in such an analysis and the usefulness of the same. The proposed algorithm uses lightweight image processing and deep learning models using convolutional layers to detect and classify the defects in both semiconductor and polymer MEMS substrates. An average cross-validation accuracy of 99.2% and high precision and recall rates of over 99% were obtained using the proposed methodology for the wafer map defect identification. The defects classified can be used by the manufacturer to extract parts of the wafer/substrate that are still usable or even utilize the defects in different devices for novel use cases.