Paul A. Morgan

Computational techniques for determining printability of real defects in EUV mask pilot line

With EUV lithography on the ITRS roadmap for sub-2X half-pitch patterning, it has become increasingly essential to ramp up efforts in being able to manufacture defect-free reticles or at least ones with minimal defects initially. For this purpose, much of the focus in recent years has been in finding ways to adequately detect, characterize, and reduce defects on both EUV blanks and patterned masks. For detection purposes, the current high-resolution DUV or e-beam inspection platforms are being extended to inspect EUV blanks and patterned masks but being non-actinic, make it very challenging to assess the real impact of the detected defects on EUV plane. Even with the realization of the EUV beta AIMS™ aerial-image based metrology in 2014-2015, the exact nature of each critical defect needs to be determined in order to be able to come up with an appropriate repair strategy. In this paper, we demonstrate the application of computational techniques to non-actinic supplemental metrology data collected on EUV mask defects to effectively determine the nature and also predict printability of these defects. The fundamental EUV simulation engine used in this approach is the EUV Defect Printability Simulator (DPS), which uses simulation and modeling methods designed specifically for the individual EUV mask components, and achieves runtimes several orders of magnitude faster than rigorous FDTD and RCWA methods while maintaining adequate accuracy. The EUV DPS simulator is then coupled with supplemental inspection and metrology measurements of real defects to effectively predict wafer printability of these defects. Several sources of such supplementary data are explored here, and may sometimes be dependent on the actual nature of defect. These sources include AFM height-profile data, SEM top-down images, and 193nm high-NA inspection images of single or multiple focus plane capture. From each of these supplemental data sources, the mask pattern and defect information is first extracted or recovered, and then forward-simulated in DPS to generate EUV aerial images subsequently analyzed for wafer printability. Each of the data sources have their strengths and limitations vis-à-vis use in a production pilot line. We exploit a mix and- match approach to effectively filter down to the defects that really matter. The 193nm inspection image data are readily available and although the pixel-sizes are somewhat coarse compared with the mask pattern widths, computationally predicting EUV printability off these images provides a quick filter of the obvious false and nuisance defects. SEM images on the other hand provide a much better two-dimensional top-down resolution of the patterns and hence work well for full-height excess or missing absorber defects but not so well for three-dimensional defects such as pits and bumps in the EUV multilayer or foreign material defects such as contamination. AFM height profile measurements generally provide the best available resolution on three-dimensional defects and thereby are well-suited for further simulations to EUV, however, AFM tip and image stability, and data acquisition time need to be comprehended. Computationally exploiting these supplemental defect inspection and metrology data in this mix-and-match approach effectively filters defects down to those that really matter on printed wafer. We see this approach as being vital to getting comprehensive defect learnings during the EUV pilot phase implementation and delivering well-characterized EUV masks to the wafer fab at substantially lower cost-of-ownership.

Computational defect review for actinic mask inspections

As optical lithography continues to extend into low-k1 regime, resolution of mask patterns continues to diminish. The limitation of 1.35 NA posed by water-based lithography has led to the application of various resolution enhancement techniques (RET), for example, use of strong phase-shifting masks, aggressive OPC and sub-resolution assist features, customized illuminators, etc. The adoption of these RET techniques combined with the requirements to detect even smaller defects on masks due to increasing MEEF, poses considerable challenges for a mask inspection engineer. Inspecting masks under their actinic-aerial image conditions would detect defects that are more likely to print under those exposure conditions. However, this also makes reviewing such defects in their low-contrast aerial images very challenging. On the other hand, inspecting masks under higher resolution inspection optics would allow for better viewing of defects post-inspection. However, such inspections generally would also detect many more defects, including printable and nuisance, thereby making it difficult to judge which are of real concern for printability on wafer. Often, an inspection engineer may choose to use Aerial and/or high resolution inspection modes depending on where in the process flow the mask is and the specific device-layer characteristics of the mask. Hence, a comprehensive approach is needed in handling defects both post-aerial and post-high resolution inspections. This analysis system is designed for the Applied Materials Aera™ mask inspection platform, all data reported was collected using the Aera.

Classification and printability of EUV mask defects from SEM images

Classification and Printability of EUV Mask Defects from SEM images EUV lithography is starting to show more promise for patterning some critical layers at 5nm technology node and beyond. However, there still are many key technical obstacles to overcome before bringing EUV Lithography into high volume manufacturing (HVM). One of the greatest obstacles is manufacturing defect-free masks. For pattern defect inspections in the mask-shop, cutting-edge 193nm optical inspection tools have been used so far due to lacking any e-beam mask inspection (EBMI) or EUV actinic pattern inspection (API) tools. The main issue with current 193nm inspection tools is the limited resolution for mask dimensions targeted for EUV patterning. The theoretical resolution limit for 193nm mask inspection tools is about 60nm HP on masks, which means that main feature sizes on EUV masks will be well beyond the practical resolution of 193nm inspection tools. Nevertheless, 193nm inspection tools with various illumination conditions that maximize defect sensitivity and/or main-pattern modulation are being explored for initial EUV defect detection. Due to the generally low signal-to-noise in the 193nm inspection imaging at EUV patterning dimensions, these inspections often result in hundreds and thousands of defects which then need to be accurately reviewed and dispositioned. Manually reviewing each defect is difficult due to poor resolution. In addition, the lack of a reliable aerial dispositioning system makes it very challenging to disposition for printability. In this paper, we present the use of SEM images of EUV masks for higher resolution review and disposition of defects. In this approach, most of the defects detected by the 193nm inspection tools are first imaged on a mask SEM tool. These images together with the corresponding post-OPC design clips are provided to KLA-Tencor’s Reticle Decision Center (RDC) platform which provides ADC (Automated Defect Classification) and S2A (SEM-to-Aerial printability) analysis of every defect. First, a defect-free or reference mask SEM is rendered from the post-OPC design, and the defective signature is detected from the defect-reference difference image. These signatures help assess the true nature of the defect as evident in e-beam imaging; for example, excess or missing absorber, line-edge roughness, contamination, etc. Next, defect and reference contours are extracted from the grayscale SEM images and fed into the simulation engine with an EUV scanner model to generate corresponding EUV defect and reference aerial images. These are then analyzed for printability and dispositioned using an Aerial Image Analyzer (AIA) application to automatically measure and determine the amount of CD errors. Thus by integrating EUV ADC and S2A applications together, every defect detection is characterized for its type and printability which is essential for not only determining which defects to repair, but also in monitoring the performance of EUV mask process tools. The accuracy of the S2A print modeling has been verified with other commercially-available simulators, and will also be verified with actual wafer print results. With EUV lithography progressing towards volume manufacturing at 5nm technology, and the likelihood of EBMI inspectors approaching the horizon, the EUV ADC-S2A system will continue serving an essential role of dispositioning defects off e-beam imaging.

Computational mask defect review for contamination and haze inspections

As optical lithography continues to extend into sub-0.35 k1 regime, mask defect inspection and subsequent review has become tremendously challenging, and indeed the largest component to mask manufacturing cost. The routine use of various resolution enhancement techniques (RET) have resulted in complex mask patterns, which together with the need to detect even smaller defects due to higher MEEFs, now requires an inspection engineer to use combination of inspection modes. This is achieved in 193nm AeraTM mask inspection systems wherein masks are not only inspected at their scanner equivalent aerial exposure conditions, but also at higher Numerical Aperture resolution, and special reflected-light, and single-die contamination modes, providing better coverage over all available patterns, and defect types. Once the required defects are detected by the inspection system, comprehensively reviewing and dispositioning each defect then becomes the Achilles heel of the overall mask inspection process. Traditionally, defects have been reviewed manually by an operator, which makes the process error-prone especially given the low-contrast in the convoluted aerial images. Such manual review also limits the quality and quantity of classifications in terms of the different types of characterization and number of defects that can practically be reviewed by a person. In some ways, such manual classification limits the capability of the inspection tool itself from being setup to detect smaller defects since it often results in many more defects that need to be then manually reviewed. Paper 8681-109 at SPIE Advanced Lithography 2013 discussed an innovative approach to actinic mask defect review using computational technology, and focused on Die-to-Die transmitted aerial and high-resolution inspections. In this approach, every defect is characterized in two different ways, viz., quantitatively in terms of its print impact on wafer, and qualitatively in terms of its nature and origin in the mask manufacturing process. The latter characterization qualifies real defect signatures, such as pin-dots or pin-holes, extrusions or intrusions, assist-feature or dummy-fill defects, writeerrors or un-repairable defects, chrome-on-shifter or missing chrome-from-shifter defects, particles, etc., and also false defect signatures, such as those due to inspection tool registration or image alignment, interlace artifacts, CCD camera artifacts, optical shimmer, focus errors, etc. Such qualitative characterization of defects has enabled better inspection tool SPC and process defect control in the mask shop. In this paper, the same computational approach to defect review has been extended to contamination style defect inspections, including Die-to-Die reflected, and non Die-to-Die or single-die inspections. In addition to the computational methods used for transmitted aerial images, defects detected in die-to-die reflected light mode are analyzed based on special defect and background coloring in reflected-light, and other characteristics to determine the exact type and severity. For those detected in the non Die-to-Die mode, only defect images are available from the inspection tool. Without a reference, i.e., defect-free image, it is often difficult to determine the true nature or impact of the defect in question. Using a combination of inspection-tool modeling and image inversion techniques, Luminescent’s LAIPHTM system generates an accurate reference image, and then proceeds with automated defect characterization as if the images were simply from a die-to-die inspection. The disposition of contamination style defects this way, filters out >90% of false and nuisance defects that otherwise would have been manually reviewed or measured on AIMSTM. Such computational defect review, unifying defect disposition across all available inspection modes, has been imperative to ensuring no yield losses due to errors in operator defect classification on one hand, and on the other, has enhanced defect characterization and detection capability of the inspection platform itself notwithstanding the number of defects detected in the process.