SYSTEMS AND METHODS FOR INSPECTING SEMICONDUCTOR DEVICES

Smart overview of the Invention

A novel defect inspection method involves capturing images of semiconductor devices at different resolutions. Initially, multiple low-resolution images of a specimen are obtained, followed by the acquisition of high-resolution images. A machine learning model is trained using these paired images to enhance the defect detection process. Subsequently, a third image is captured from another specimen, which is then processed through the trained model to generate a corresponding high-resolution image.

Importance of Inspection in Semiconductor Manufacturing

Inspection plays a critical role in semiconductor manufacturing, especially as device dimensions shrink. Detecting defects early in the fabrication process helps ensure higher yields and profitability. Traditional methods often involve high-magnification systems for detailed defect analysis; however, these can be time-consuming and potentially damaging to the specimens being inspected.

Defect Review and Metrology Processes

Defect review processes involve re-evaluating defects identified during initial inspections to gather detailed information about them. In contrast, metrology processes focus on measuring specific characteristics of semiconductor features, independent of defect presence. Both processes are essential for maintaining quality control in manufacturing but differ significantly in their objectives and methodologies.

Challenges with Existing Inspection Techniques

Current inspection systems frequently fall short in providing the high-resolution data necessary for effective defect analysis and metrology. High-resolution imaging can be detrimental to active specimens due to potential damage and operational interference. Consequently, there is a need for improved systems that can efficiently produce high-resolution images without compromising the integrity of the inspected devices.

Innovative Approach Using Machine Learning

The proposed system leverages machine learning to convert low-resolution images into high-resolution counterparts without directly capturing high-resolution data from active specimens. By training on a set of paired images from dummy wafers, this approach enhances throughput while preserving the inspected specimens. This innovative method addresses previous limitations and offers a more effective solution for semiconductor device inspection.