As mask pattern feature sizes shrink the need for tighter control of factors affecting critical dimensions (CD) increases
at all steps in the mask manufacturing process. To support this requirement Intel Mask Operation is expanding its
process and equipment monitoring capability. We intend to better understand the factors affecting the process and
enhance our ability to predict reticle health and critical dimension performance.
This paper describes a methodology by which one can predict the contribution of the dry etch process equipment to
overall CD performance. We describe the architecture used to collect critical process related information from various
sources both internal and external to the process equipment and environment. In addition we discuss the method used to
assess the significance of each parameter and to construct the statistical model used to generate the predictions. We
further discuss the methodology used to turn this model into a functioning real time prediction of critical dimension
performance. Further, these predictions will be used to modify the manufacturing decision support system to provide
early detection for process excursion.
The reticle manufacturing process induces various defects on the mask that need to be repaired. Missing absorber or clear defects are often repaired by depositing a carbon-based material (depo) using a Focused Ion Beam (FIB) tool. Few cases of such depo repairs on defects in between nested contacts on attenuated phase shift masks were found to fail upon use in high volume wafer manufacturing factories. With the goal of first reproducing the problem in the mask shop, a controlled set of depo repairs were performed on a test reticle and sequentially exposed on a DUV flood exposure system, emulating stepper exposure. The repair AIMSTM printability and AFM height profiles were measured before and after each exposure step. With incremental exposures, AIMSTM results showed the repaired contacts gradually printing larger in size and AFM results showed the tail of the depo repair (also referred to as depo overspray or halo) correspondingly receding with exposure. This suggests that the tail of the depo presumably contributes to the correct print CD of the repaired contact, and its gradual recession with exposure was likely causing the contacts to print larger, ultimately even bridging with the neighboring nested contact in some cases. This mechanism was confirmed by checking similar repairs on several production masks already being used in the wafer factories, at different stages of exposure. Subsequently, a novel post-repair process was developed which achieves rapid overspray removal thereby avoiding any further change in these repairs and associated wafer yield impact upon prolonged use on scanners.
We have installed the industry's first commercial electron beam mask repair tool in Intel's mask shop. In this paper we describe our on-going efforts of developing e-beam repair processes for binary, phase-shifting and EUVL masks. We present a complete characterization of fundamental capabilities of e-beam repair and make general comparisons with other technologies, in terms of repair resolutions, substrate damage, edge placement, removal selectivity, and process margin. Among many applications, results from quartz etch with excellent resolution and vertical profile are described.
This article presents the evolution of the first fully automated simulation based mask defect dispositioning and defect management system used since late 2002 in a production environment at Intel Mask Operation (IMO). Given that inspection tools flag defects which may or may not have any lithographic significance, it makes sense to repair only those defects that resolve on the wafer. The system described here is a fully automated defect dispositioning system, where the lithographic impact of a defect is determined through computer simulation of the mask level image. From the simulated aerial images, combined with image processing techniques, the system can automatically determine the actual critical dimension (CD) impact (in nanometers). Then, using the product specification as a criteria, can pass or fail the defect. Furthermore, this system allows engineers and technicians in the factory to track defects as they are repaired, compare defects at various inspection steps and annotate repair history. Trends such as yield and defect commonality can also be determined. The article concludes with performance results, indicating the speed and accuracy of the system, as well as the savings in the number of defects needing repair.
Tebaldi is a software tool developed at Intel Mask Operation (IMO) for quantitatively analyzing patterns in 2D. Its initial scope was to analyze aerial images taken with a microscope. However the software has recently been enhanced to support aerial images obtained through simulation, bitmap, jpeg and tiff files saved from the mask inspection systems and the scanning electron microscope (SEM). This article primarily focuses on the SEM module of the software. Tebaldi supports simulated aerial images generated through IMO’s simulation based defect disposition system. This allows engineers to directly correlate 2D structures in an experimental aerial image, with those in a simulated image. To analyze SEM images, the software features scaling, alignment and calibration functions. Several linear and non-linear filtration techniques to reduce noise and charging exist. Custom convolution kernels can be user defined. Ability to segment features and extract contours also exist. Further, these contours can be overlayed and shortest distances between corresponding points can be computed in a user friendly manner with a high degree of confidence. Tebaldi is currently used in production to disposition defects in repaired sites on masks shipped from IMO as well as to compare SEM images to determine the pattern fidelity across mask writers and processes within IMO.
In this paper, we present the test results obtained from the first commercial electron beam mask repair tool. Repaired defect sites on chrome-on-glass masks are characterized with 193nm AIMS to quantify the edge placement precision as well as optical transmission loss. The electron beam mask repair tool is essentially based on a scanning electron microscope (SEM), therefore, it can be used for in-situ CD and defect metrology. E-beam for EUV mask defect repair is also discussed. These early results are very encouraging and demonstrate the basic advantages of electron beam mask repair as well as highlight the key challenge of charge control.
In this work, we are reporting on a lithography-based methodology and automation in the design of Program Defect masks (PDM’s). Leading edge technology masks have ever-shrinking primary features and more pronounced model-based secondary features such as optical proximity corrections (OPC), sub-resolution assist features (SRAF’s) and phase-shifted mask (PSM) structures. In order to define defect disposition specifications for critical layers of a technology node, experience alone in deciding worst-case scenarios for the placement of program defects is necessary but may not be sufficient. MEEF calculations initiated from layout pattern data and their integration in a PDM layout flow provide a natural approach for improvements, relevance and accuracy in the placement of programmed defects. This methodology provides closed-loop feedback between layout and hard defect disposition specifications, thereby minimizing engineering test restarts, improving quality and reducing cost of high-end masks. Apart from SEMI and industry standards, best-known methods (BKM’s) in integrated lithographically-based layout methodologies and automation specific to PDM’s are scarce. The contribution of this paper lies in the implementation of Design-For-Test (DFT) principles to a synergistic interaction of CAD Layout and Aerial Image Simulator to drive layout improvements, highlight layout-to-fracture interactions and output accurate program defect placement coordinates to be used by tools in the mask shop.
Mask quality is a prime concern to the Intel Mask Operation (IMO) and the Intel wafer fabrication customers. Extreme concern is taken to inspect and repair all defects before shipment. Given that the classification and repair of defects detected by inspection systems is labor intensive, the procedure is prone to human error. Futhermore, since operators manually disposition hundreds of defects each day, it is virtually impossible to eliminate all misclassifications. Due to diffraction effects, not all defects resolve on a wafer. Hence, a defect that an operator may classify as 'real' may indeed be 'lithographically insignifincant'. Conversely an operator may miss a defect that prints, causing a serious reduction in product yield. The DIVAS (Defect, Inspection, Viewing, Archiving and Simulation) system has been described previously and was developed to address these manual classification issues. This paper outlines the fully automated system deployed in a production environment.
Today's reticle inspection tools can provide a wealth of information about defects. We introduce here a system called DIVAS: Defect Inspection Viewing, Archiving, and Simulation that fully uses and efficiently manages this wealth of defect information. In this paper, we summarize the features of DIVAS and describe in more detail PRIMADONNA, one of its components. Current reticle defect specifications are based, primarily, on defect size. Shrinking design rules, increasing MEEF and use of Optical Enhancement Techniques cause size to be an inadequate criterion for disposition. Furthermore, visual disposition of defects is not automated, strictly reproducible, or directly tied to wafer lithography. To compensate for these inadequacies, reticle specifications are set conservatively adding direct and hidden costs to the manufacturing process. PRIMADONNA, utilizing Prolith as the simulation engine, retrieves all defect and reference images saved from a KLA SLF77 inspection tool and processes them through a series of increasingly rigorous simulation stages. These include pre-filtering, aerial image formation, and post filtration. Difference metrics are used to quantify a defect's wafer impact. We will report results comparing PRIMADONNA decisions to manual classifications for a significant volume of inspections. Correlation between PRIMADONNA results and AIMS metrology will be presented.
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