Rick Lai

AIMS D2DB simulation for DUV and EUV mask inspection

AIMS™ Die-to-Die (D2D) is widely used in checking the wafer printability of mask defects for DUV lithography. Two AIMS images, a reference and a defect image, are captured and compared with differences larger than certain tolerances identified as real defects. Since two AIMS images are needed, and since AIMS system time is precious, it is desirable to save image search and capture time by simulating reference images from the OPC mask pattern and AIMS optics. This approach is called Die-to-Database (D2DB). Another reason that D2DB is desirable is in single die mask, where the reference image from another die does not exist. This paper presents our approach to simulate AIMS optics and mask 3D effects. Unlike OPC model, whose major concern is predicting printed CD, AIMS D2DB model must produce simulated images that match measured images across the image field. This requires a careful modeling of all effects that impact the final image quality. We present a vector-diffraction theory that is based on solid theoretical foundations and a general formulation of mask model that are applicable to both rigorous Maxwell solver and empirical model that can capture the mask 3D-effects. We demonstrated the validity of our approach by comparing our simulated image with AIMS machine measured images. We also briefly discuss the necessary changes needed to model EUV optics. Simulation is particularly useful while the industry waits for an actinic EUV-AIMS tool.

Implementation of an efficient defect classification methodology for advanced reticle inspection

As design rule continues to shrink towards ITRS roadmap requirements, reticle defect capture criteria are becoming ever more challenging. Pattern fidelity and reticle defects that were once perceived as insignificant or nuisance are now becoming a significant considerable yield impacting factor. More defects are also detectable and presented with increase in implementation of new generation reticle inspection systems. Therefore, how to review and characterize defects accurately and efficiently is becoming more significant. In particular, defect classification time often corresponds directly to the cost and the cycle time of mask manufacturing or new technology development. In this study we introduce a new mask defect review tool called ReviewSmart, which retrieves and processes defect images reported from KLA-Tencors high sensitivity TeraScan inspection tool. Compared to the traditional defect review method, ReviewSmart provides a much better method to manage defects efficiently by utilizing the concept of defect grouping disposition. Through the application and qualification results with respectable reticle production cases, the implementation of ReviewSmart has been proven to be effective for reducing defect classification loading and improving defect characterizing efficiency. Moreover, the new review tool is helpful to categorically identify tool or process variations thus allowing users to expedite the learning process for developing production worthy leading node processes.

Improvement in defect classification efficiency by grouping disposition for reticle inspection

As the lithography design rule of IC manufacturing continues to migrate toward more advanced technology nodes, the mask error enhancement factor (MEEF) increases and necessitates the use of aggressive OPC features. These aggressive OPC features pose challenges to reticle inspection due to high false detection, which is time-consuming for defect classification and impacts the throughput of mask manufacturing. Moreover, higher MEEF leads to stricter mask defect capture criteria so that new generation reticle inspection tool is equipped with better detection capability. Hence, mask process induced defects, which were once undetectable, are now detected and results in the increase of total defect count. Therefore, how to review and characterize reticle defects efficiently is becoming more significant. A new defect review system called ReviewSmart has been developed based on the concept of defect grouping disposition. The review system intelligently bins repeating or similar defects into defect groups and thus allows operators to review massive defects more efficiently. Compared to the conventional defect review method, ReviewSmart not only reduces defect classification time and human judgment error, but also eliminates desensitization that is formerly inevitable. In this study, we attempt to explore the most efficient use of ReviewSmart by evaluating various defect binning conditions. The optimal binning conditions are obtained and have been verified for fidelity qualification through inspection reports (IRs) of production masks. The experiment results help to achieve the best defect classification efficiency when using ReviewSmart in the mask manufacturing and development.

Megasonic cleaning strategy for sub-10nm photomasks

One of the main challenges in photomask cleaning is balancing particle removal efficiency (PRE) with pattern damage control. To overcome this challenge, a high frequency megasonic cleaning strategy is implemented. Apart from megasonic frequency and power, photomask surface conditioning also influences cleaning performance. With improved wettability, cleanliness is enhanced while pattern damage risk is simultaneously reduced. Therefore, a particle removal process based on higher megasonic frequencies, combined with proper surface pre-treatment, provides improved cleanliness without the unintended side effects of pattern damage, thus supporting the extension of megasonic cleaning technology into 10nm half pitch (hp) device node and beyond.

Automated Defect Classification (ADC) and Progression Monitoring (DPM) in wafer fab reticle requalification

As optical lithography continues to extend into low-k1 regime, resolution of mask patterns continue to diminish, and so do mask defect requirements due to increasing MEEF. Post-inspection, mask defects have traditionally been classified by operators manually based on visual review. This approach may have worked down to 65/55nm node layers. However, starting 45nm and smaller nodes, visually reviewing 50 to sometimes 100s of defects on masks with complex modelbased OPC, SRAF, and ILT geometries, is error-prone and takes up valuable inspection tool capacity. Both these shortcomings in manual defect review are overcome by adoption of the computational solution called Automated Defect Classification (ADC) wherein mask defects are accurately classified within seconds and consistent to guidelines used by production technicians and engineers.

Efficiency and throughput improvement on defect disposition through automated defect classification

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. Luminescents 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.

Enabling virtual wafer CD (WCD) using inverse pattern rendering (IPR) of mask CD-SEM images

A wafers printed CD error can be impacted by unaccounted mask making process variation. Unaccounted mask CD and/or corner rounding alters the intended drawn mask pattern contributing to a wafers printed CD error. During OPC wafer calibration, average mask bias and corner rounding are accounted for in the OPC model, but random local mask making process variations or mask-to-mask variations can be difficult to account in such model calibration. Thus when a wafers CD has error, it can be difficult to determine if the general root cause was due to mask or wafer or both. An in-line monitoring application has been developed to extract accurate mask CD and rendered mask polygon from collected mask CD-SEM images. Technical information will be presented on the challenges of accurately extracting information from SEM images. In particular, discussions include SEM image calibration, contour extraction, inverse pattern rendering, and general image processing to account for mask SEM aberrations (translation, rotation, & dilation), tool-to-tool variation, vendor-to-vendor variation, run-to-run variation, and dark/bright field pattern-to-pattern variation. After accurate mask SEM contours are obtained, lithographic simulations are performed on extracted polygon contours to determine the impact of mask variation on wafer CD. This paper will present detail information about the Inverse Pattern Rendering (IPR) capabilities developed for a virtual Wafer CD (WCD) application and its results, which is proven to achieved 0.5 nm accuracy across multiple critical layers from 28 nm to 40 nm nodes on multiple CD-SEM tools over multiple mask shop locations.

Photomask defect tracing, analysis and reduction with chemically amplified resist process

The features of optical proximity correction are becoming very aggressive as production technology migrates into 90nm/130 nm regime. The complicated optical proximity correction (OPC) patterns often result in un-repairable defects, a major yield loss mechanisms in a mask production line. Defect control is increasingly important. A methodology for identifying defect sources and reduction is demonstrated in this paper. The mechanisms and causes of defect formation could be determined with corresponding process step on the strength of sequence inspections. The cause of half-etched opaque defect on negative CAR process was found from PR fragment contamination of e-beam exposure step. After clean-up of e-beam chamber, yield was increased over 20%. Big pinhole defect and contact of AttPSM positive process was found on ADI step. The possible cause was poor CAR adhesion. These two type defects were decreased by modification of developing recipe, special on rinse step. Design experiment with Taguchi method was used to optimize the interactive recipe of plasma descum and rinse step on developing step of implanted layer. Average defect density was decreased from 0.99 to 0.27, and percentage of zero defect rate has been increased from 29.5 to 63.3%.

Reticle inspection optimization for 90-nm and 130-nm technology nodes using a multibeam UV wavelength inspection tool

As the lithography design rule of IC manufacturing industry migrates into sub-130nm nodes, low k1 factor prevails, the mask error enhancement factor (MEEF) increases. Low k1 processing calls for aggressive sub-resolution assist features and the use of attenuated phase shift masks (AttPSMs). The aggressive OPC features pose challenges to reticle inspection due to high false detection, which is time-consuming for defect classification and impacts the throughput of mask manufacturing. Moreover, the high transmission of the shifter material of 193 nm AttPSM also challenges the UV-based reticle inspection tools with high nuisance counts due to undesirable optical diffraction effects. For a given reticle inspection tool, it is necessary to calibrate the system contrast between the clear and opaque regions (quartz/chrome or quartz/MoSi) of the reticles. In this study, we present the influences of various calibration conditions on sensitivity, false and nuisance detection of reticle inspections. Both the STARlight contamination inspections and the die-to-die pattern inspections were carried out using the KLA-Tencor TeraStar inspection tools with production masks and programmed defect test masks including binary intensity masks (BIMs) and AttPSMs. Successful applications with low false detection and adapted sensitivity will be illustrated in terms of optimizing the calibration setup.