Virtual Metrology in Semiconductor Fabrication Foundry Using Deep Learning Neural Networks

Abstract

Physical metrology inspections are crucial in semiconductor fabrication foundry to ensure wafers are fabricated within the production specification limits and prevent faulty wafers from being shipped and installed in customers' devices. However, it is not possible to examine every wafer as such inspection would incur impractical cost on manpower, finances, and production cycle time (CT) of fabrication foundries (fabs). Virtual metrology (VM) presents an alternate approach to perform metrology inspection without incurring high costs by using machine learning (ML) models. By leveraging historical equipment and process data, ML models can be calibrated to estimate the targeted metrology variables to estimate the quality of wafers, thereby performing virtual inspection on wafers. Recently, VM researchers begin introducing deep learning (DL) into VM research works to examine its capability. Specifically, the VM researchers experimented on the convolutional neural network (CNN). The targeted metrologies are metrologies of plasma-based processes in both etching and chemical vapor deposition. Initial success has been reported by the VM researchers. While various CNN-based VM models have been proposed plasma-based fabrication processes, it has yet to be experimented in photolithography process. Motivated by the initial successes of CNN in plasma-based processes, this work is an initiative to experiment CNN's performance in predicting the overlay errors of photolithography process. Using data from a real fab, this work first establishes a baseline using the methodology of a prior work. Then, the prediction results of the proposed CNN model are compared with the baseline. The results showed that CNN is able to further reduce the prediction errors.

An Objective Metallographic Analysis Approach Based on Advanced Image Processing Techniques

“…For instance, there are recent developments in DL applied to microscopy with the aim of classifying and detecting anatomical elements in the wood [14], helping in cancer detection [15], or illustrating the techniques with images from microscopes [16]. DL is widely used for classifying and detecting defects in extracted images [17] and for metrology measurements, such as in this specific work focusing on semiconductors [18].…”