Mask inspection

About: Mask inspection is a research topic. Over the lifetime, 1072 publications have been published within this topic receiving 8696 citations.

Inspection method and apparatus for mask defect, and method for creating reference for mask inspection

Abstract: PROBLEM TO BE SOLVED: To automate defect inspection in an apparatus for defect inspection of an exposure mask and to realize high accuracy and high-speed defect inspection. SOLUTION: The method includes the steps of: acquiring a detected defect pattern image, a reference pattern image, and inspection data images corresponding to the respective defect pattern image and reference pattern image; comparing the defect dimensional difference and the pattern area difference between the defect pattern image and the reference pattern with preliminarily determined thresholds to extract a pattern deformation defect; calculating the gravity center of each pattern image; extracting the size of a first vector connecting the gravity centers of the defect pattern image and the corresponding inspection data image, the size of a second vector connecting the gravity centers of the reference pattern image and the corresponding inspection data image, and the angle made by the first and second vectors; comparing the obtained values with preliminarily determined thresholds; extracting a pattern misalignment defect; and automatically judging whether the pattern deformation defect and the pattern misalignment defect affects the transferring property or not. COPYRIGHT: (C)2006,JPO&NCIPI

Inspection system and inspection method

Abstract: An inspection system determines, for each detected pattern defect, a defect inspection pattern area of predetermined dimensions containing the coordinates of the defect, then determines the clusters or cells whose reference points are located within the defect inspection pattern area. The system extracts the data of these clusters or cells from design pattern data read from a first magnetic disk unit. The system then generates an output file containing the extracted data. The output file is then converted into the same format as the input design pattern data or into OASIS format, before it is output to a second magnetic disk unit. The extracted pattern data specifying the clusters or cells within each defect inspection pattern area can be output from the mask inspection system to external systems.

Highly Reliable 198 nm Light Source for Semiconductor Inspection Based on Dual Fiber Lasers

Abstract: Highly reliable DUV light sources are required for semiconductor applications such as a photomask inspection. The mask inspection for the advanced devices requires the UV lightning wavelength beyond 200 nm. By use of dual fiber lasers as fundamental light sources and the multi-wavelength conversion we have constructed a light source of 198nm with more than 100 mW. The first laser is Yb doped fiber laser with the wavelength of 1064 nm; the second is Er doped fiber laser with 1560 nm. To obtain the robustness and to simplify the configuration, the fundamental lights are run in the pulsed operation and all wavelength conversions are made in single-pass scheme. The PRFs of more than 2 MHz are chosen as an alternative of a CW light source; such a high PRF light is equivalent to CW light for inspection cameras. The light source is operated described as follows. Automatic weekly maintenance within an hour is done if it is required; automatic monthly maintenance within 4 hours is done on fixed date per month; manufacturer's maintenance is done every 6 month. Now this 198 nm light sources are equipped in the leading edge photomask inspection machines.

Printability of buried extreme ultraviolet lithography photomask defects

Abstract: Native acting phase-programmed defects, otherwise known as buried program defects, with attributes very similar to native defects, were successfully fabricated using a high-accuracy overlay technique. The defect detectability and visibility were analyzed with conventional amplitude and phase-contrast blank inspection at 193-nm wavelength, pattern inspection at 193-nm wavelength, and scanning electron microscopy. The mask was also printed on wafer, and printability is discussed. Finally, the inspection sensitivity and wafer printability are compared, leading to the observation that the current blank- and pattern-inspection sensitivity is not enough to detect all of the printable defects.

Efficiency and throughput improvement on defect disposition through automated defect classification

Abstract: The routine use of aggressive OPC at advanced technology nodes, i.e., 40nm and beyond, has made photomask patterns quite complex. The high-resolution inspection of such masks often result in more false and nuisance defect detections than ever before. Traditionally, each defect is manually examined and classified by the inspection operator based on defined production criteria. The significant increase in total number of detected defects has made manual classification costly and non-manufacturable. Moreover, such manual classification is also susceptible to human judgment and hence error-prone. Luminescent's Automated Defect Classification (ADC) offers a complete and systematic approach to defect disposition and classification. The ADC engine retrieves the high resolution inspection images and uses a decision-tree flow based on the same criteria human operators use to classify a given defect. Some identification mechanisms adopted by ADC to characterize defects include defect color in transmitted and reflected images, as well as background pattern criticality based on pattern topology. In addition, defect severity is computed quantitatively in terms of its size, impacted CD error, transmission error, defective residue, and contact flux error. The final classification uses a matrix decision approach to reach the final disposition. In high volume manufacturing mask production, matching rates of greater than 90% have been achieved when compared to operator defect classifications, together with run-rates of 250+ defects classified per minute. Such automated, consistent and accurate classification scheme not only allows for faster throughput in defect review operations but also enables the use of higher inspection sensitivity and success rate for advanced mask productions with aggressive OPC features.