A trainable Die-To-Database for fast e-Beam inspection: learning normal images to detect defects

Abstract

In the drive toward sub-10-nm semiconductor devices, manufacturers have been developing advanced lithography technologies such as extreme ultraviolet lithography and multiple patterning. However, these technologies can cause unexpected defects, and a high-speed inspection is thus required to cover the entire surface of a wafer. A Die-to-Database (D2DB) inspection is commonly known as a high-speed inspection. The D2DB inspection compares an inspection image with a design layout, so it does not require a reference image for comparing with the inspection image, unlike a die-to-die inspection, thereby achieving a high-speed inspection. However, conventional D2DB inspections suffer from erroneous detection because the manufacturing processes deform the circuit pattern from the design layout, and such deformations will be detected as defects. To resolve this issue, we propose a deep-learning-based D2DB inspection that can distinguish a defect deformation from a normal deformation by learning the luminosity distribution in normal images. Our inspection detects outliers of the learned luminosity distribution as defects. Because our inspection requires only normal images, we can train the model without defect images, which are difficult to obtain with enough variety. In this way, our inspection can detect unseen defects. Through experiments, we show that our inspection can detect only the defect region on an inspection image.