Automated aerial image based CD metrology initiated by pattern marking with photomask layout data

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1 Automated aerial image based CD metrology initiated by pattern marking with photomask layout data Grant Davis 1, Sun Young Choi 2, Eui Hee Chung 2, Arne Seyfarth 3, Hans van Doornmalen 3, Eric Poortinga 4 1 Mentor Graphics Corp., San Jose, CA USA 2 Samsung Electronics Co. Ltd., Korea 3 Carl Zeiss SMS GmbH, Jena, Germany 4 Carl Zeiss SMT Inc., Peabody, MA USA ABSTRACT The photomask is a critical element in the lithographic image transfer process from the drawn layout to the final structures on the wafer. The non-linearity of the imaging process and the related MEEF impose a tight control requirement on the photomask critical dimensions. Critical dimensions can be measured in aerial images with hardware emulation. This is a more recent complement to the standard scanning electron microscope measurement of wafers and photomasks. Aerial image measurement includes non-linear, 3-dimensional, and materials effects on imaging that cannot be observed directly by SEM measurement of the mask. Aerial image measurement excludes the processing effects of printing and etching on the wafer. This presents a unique contribution to the difficult process control and modeling tasks in mask making. In the past, aerial image measurements have been used mainly to characterize the printability of mask repair sites. Development of photomask CD characterization with the AIMS tool was motivated by the benefit of MEEF sensitivity and the shorter feedback loop compared to wafer exposures. This paper describes a new application that includes: an improved interface for the selection of meaningful locations using the photomask and design layout data with the Calibre Metrology Interface, an automated recipe generation process, an automated measurement process, and automated analysis and result reporting on a Carl Zeiss AIMS system. 1. INTRODUCTION Photomask metrology activities have traditionally been based on measuring the physical dimensions of printed features. Optical-based photomask metrology systems have given way to critical dimension scanning electron microscopes (CD- SEM), which exhibit superior imaging resolution and stability. Utilizing a top-down measurement approach, CD-SEMs provide a quick and accurate measurement. Results are then analyzed to show CD uniformity (CDU), mean-to-target (MTT), and linearity performance of the photomask. Photomask makers are given specifications for these parameters that must be met by the mask manufacturing process. These specifications are driven by the ever-increasing complexity of photolithography needed to meet device performance targets. Wafer fab engineers generally speak about Across Chip Linewidth Variation (ACLV), which describes the CD variation across a single chip on the wafer. CD variation, when large enough, begins to degrade the chip electrical performance, even to the point of non-functionality 1. It is important to understand the contributors in the manufacturing process that make up ACLV. Concentrating on photomask manufacture, we will not consider here the wafer manufacturing effects of resist and etch processing. The remaining contributors that have been identified are typically grouped into two separate categories: imaging variation and photomask variation.

2 Imaging variation includes focus error, dose error, optical aberrations, and flare 2. Due to the nature of lithography, identifying the amount of imaging variation contributing to ACLV can be difficult. Research has been reported of replacing the wafer with an aerial image sensor as a measurement option. A more realistic approach is to fully characterize the ACLV contribution from photomask variation and subtract that value from the total ACLV to arrive at an approximation for imaging variation. The photomask variation component of ACLV includes factors such as CD uniformity, phase error, transmission error, sidewall angle deviation, surface roughness, and material properties such as birefringence. Since the photomask is typically manufactured by a vendor, the wafer fab minimizes the photomask contribution to ACLV by specifying strict CD uniformity (CDU) performance when the order is placed. The past assumptions of a simple relationship between photomask uniformity and ACLV have broken down with the extension of technology past previous optical limits with phase shifting techniques (PSM), reticle enhancement techniques (RET) and advanced scanner illumination patterns. The number of these factors continues to increase. For example, it has also been observed that the increased incident angle of illumination due to large numerical aperture imaging with off-axis illumination influences the Mask Error Enhancement Factor (MEEF), as seen in Figure-1. The photomask topography becomes a significant factor in the MEEF of very small features. The successful application of aerial image emulation for CDU measurement has been demonstrated previously, as a method to include many of the complex imaging factors into the mask CD measurement (Poortinga, et al. 6). An additional example of the practical application of AIMS TM CD measurement is shown in Figure-2. The next logical step is the development of application enhancements automating measurement setup using the mask and design data, within the demands of a production environment. Traditionally, aerial image metrology systems are used to evaluate defect printability and repair success. The measurement coordinates are provided by a photomask inspection system or repair tool. Throughput is not necessarily a high priority for defect printability because the number of measurements is typically low. The analysis of defects and repairs is normally completed manually by an engineer. Using aerial image metrology for production CD measurement requires a different approach. To maximize the benefit of aerial image based CD metrology, it is advantageous to use the photomask layout as the initiator of the measurement process. Knowing that different photomask patterns print differently, it makes sense to measure only those features that have the highest potential to contribute to CD variation. Once identified, the positions of those features can be used to set up the measurement process. This motivates the development of a direct link from the design data into the measurement setup. Throughput becomes a primary concern as the demand for CD measurements is much greater than for defect evaluation, leading to the requirement for automation of setup and measurement. Increasing efficiency further requires the analysis to also be completed automatically. Lastly, the generated result data should then be presented to the user in a way that allows quick and concise evaluation of the photomask performance. 2. SOFTWARE DEVELOPMENT PROCESS The project described in this paper sets out to explain how the process of mask design verification is implemented and how it is used. The process flow is separated into two distinct sections, one used to setup the measurement positions as a Front-end Job Specification using the design data, and the other is a Back-end Measurement Module for measurement acquisition and analysis. As seen in Figure-3, the process flow is cyclical, with the measurement results being used to make manufacturing decisions and potentially used to influence further design verification measurements. 2.1 Front-end Job Specification The front-end job specification system is the Calibre Metrology Interface (CMi). It runs on an off-line workstation to create measurement input files, which the Zeiss AIMS DFM system uses to make automated aerial image CD measurements. CMi runs within Calibre WORKbench, a layout viewer from Mentor Graphics that provides aerial image simulation and model development, as well as graphical viewing, processing, and editing (Figure-6). A new

3 library of Metrology Application Programming Interface (MAPI) functions (part of the WORKbench Tcl programming language) has been used to construct CMi. The fundamental requirement for CMi is to create specific instructions for the AIMS system to collect large numbers of CD measurements. The system must be robust, efficient, and accurate. The two major inputs to CMi are measurement locations and the mask layout. The sources of measurement locations are varied. For example, tools like OPC verification may identify hot-spot locations from analysis of the layout 7, or designers may pass on layout knowledge in a list of coordinates on a sheet of paper. The sources have been reduced to three basic alternatives for location marking input into CMi: 1) interactive drawings 2) a coordinate list (CSV spreadsheet file Figure 5) 3) GDS file shapes These alternatives provide convenient interfaces to use the results from Calibre OPCverify, and a variety of other Mentor Calibre analysis tools. Marking is best done in the chip layout, where multiple layers are available for guidance, and cell hierarchy is present for economy. When the other layers of the chip layout are present, the user can identify critical structures more easily. For example, the gate regions are formed where poly layer crosses the diffusion layer. Since the output context for mask measurement is the mask layout, a standard MEBES jobdeck is loaded into the viewer. The fractured mask patterns are visible in the jobdeck. The multi-layer hierarchical chip layout files (standard OASIS or GDS) can be overlayed onto the mask for additional marking guidance. In addition, the chip hierarchy provides a convenient and efficient framework for marking repeated measurements. Measurement locations that are input in the chip context are automatically converted to mask context by CMi. The chip layout is not required, however. Once the locations and layout are identified, the layout view is updated, and the user may graphically review the job specifications. Spreadsheet-input Markers and Regions are rendered into the layout for review, along with those present in the input layout. Any existing markers may be adjusted in the layout, or more may be drawn by the user at this point. When the inputs have been accepted, the translation is started. First, the marked locations are validated for measurement clearances in the mask layout. Any specified searches for measurement locations are also performed. Then, aerial images are automatically simulated, using the same optical conditions as AIMS, to be used during the AIMS acquisition process. The resulting validated locations can be graphically reviewed, and the job modified and re-run to the user's satisfaction. The output from CMi is an AIMS measurement input file written in standard XML format. It includes the specifications of all capture and measurement locations. Group and region designations are attached to the locations, and then transferred to the measurement tool to facilitate later analysis. Alignment locations and images are included. A critical development item was the format of the measurement input file interface from CMi to AIMS. Due to its simplicity and industry acceptance, the XML format was chosen. 2.2 Back-end Measurement Module An important element is the consideration of the steps required at the AIMS as an embedded part of the whole process. The AIMS software implements the process in three steps. The first step is referred to as the DFM Job Assembler. It creates, based on the given input data from Mentor Graphics CMi software, a so-called AIMS job. That job controls the remaining DFM processes. All parameters (for example, the lithographic settings, which are necessary for the capture process, but not contained inside the CMi output file) are supplied by a predefined template. That template is a common AIMS recipe, which is selected by the user. Recipes are commonly shared between jobs.

4 The second step involves a series of dialog windows referred to as the DFM Image Capture Wizard. The wizard acquires AIMS images based on the created AIMS Job file in an automatic process. A special feature of that process is the possibility of increasing positioning accuracy by using a local alignment before final image acquisition. This feature increases the stage position accuracy by using a simulated aerial image as an alignment image for each capture field. The alignment image and its position in mask coordinates are included in the measurement input file, which was generated by the CMi software previously. After capturing an AIMS image, the geometric difference vector between the known position of the simulated image and the real stage position is calculated by a correlation algorithm. Then the stage is re-positioned regarding this difference vector. The position correction is necessary to allow a fully automated process of extracting measurement values in the following process step. It can also be disabled for systems that have stages with a sufficient positional precision. Another special feature is the support of a new type of AIMS image file. The new file type (with the extension.ai3 ) is able to store more than 4GB of data. The traditional AIMS file format (.msm) is limited due to its architecture. Using the new file type, it is possible to capture a large number of AIMS images in a single measurement run. The third step involves the DFM Measure Analysis Wizard. It extracts the measured values in a fully automated process from the captured AIMS images of the previous step. The defined measurement positions, which indicate where the measurement slices have to be drawn, are also part of the DFM input data set from the Mentor Graphics CMi software. To ensure the necessary positioning accuracy, the local alignment is repeated in this step. Using the same correlation algorithm as before, the remaining difference vector between the simulated alignment image and the captured AIMS image is determined. The placement of the measurement slices is shifted by that difference vector. The local alignment is executed individually for every captured AIMS image. All slices are then placed exactly at the intended positions. For every measure position, analysis is performed to provide the following values: a set of CD values (one per image plane and measure position) a set of NILS values ( Normalized Image Log Slope - one per image plane and measure position) a Depth of Focus (DOF) value dependent on a given, but changeable EL value an Exposure Latitude (EL) value dependent on a given, but changeable DOF value Concerning the new calculation of CD- and NILS- values for all image planes, it is now possible to iterate through the relating CD- and NILS- maps using the DFM Measure Analysis Wizard. The new Exposure Latitude-map can be adjusted by changing the related DOF value. The new DOF-map can be adjusted by changing the related EL value. All measured values (CD, NILS, EL, DOF), all relevant input values (target-cd values, CD-tolerance values, analysis group names), and all results of the comparison between target value and measured CD value (including tolerance range) are shown in a comprehensive result data table. Additionally, there is a short statistical overview containing the minimum value, the maximum value, and the mean value for every type of measured value. There are three kinds of output at the third step: map image, result table, and modified job definition. Every map can be manually exported as a bitmap (.bmp) image file. The result table can be exported as a set of CSV table files one per image plane. Both types of files are generated automatically at the end of this process. It is also possible to store additional snapshots for manually adjusted positions both as bitmap files and as CSV files. Every measurement can be assigned to one or multiple analysis groups, which were defined with the CMi front-end software. Measurements can be enabled or disabled by group during the analysis phase at the AIMS software, as well as in subsequent analysis steps. It is convenient to import the generated CSV result file for viewing into a spreadsheet application like Microsoft Excel. The third kind of output is a modified job definition XML file which can be optionally used in a subsequent measurement run as a modification of the CMi-generated input data set. It replaces the target CD values for the next run with the current result CD values. This option makes it possible to observe a photomask performance over time by executing periodically both Step-2 and Step-3. The first and third step do not need any direct access to an AIMS tool and can be done at an engineer s desktop. Only the second step the image acquisition step has to be executed at the AIMS tool. Tool utilization can be optimized by performing the setup and analysis steps off-line.

5 3. RESULTS Validation of the proposed metrology process methodology and software development work was completed using a Mentor Graphics Calibre WORKbench workstation and a Carl Zeiss AIMS fab193i system within a photomask manufacturing facility. This system is capable of measuring advanced photomasks down to the 65nm node using immersion lithography settings up to 93NA. The performance of the AIMS fab193i is well documented in published work by Zibold, et al 5. The overall flow is shown in Figure-2. Using the layout of a production photomask, measurement was opened using WORKbench and the CMi module (Figure-4). Measurement locations were drawn within the chip (Figure-3 and Figure-4) on the photomask and then arrayed to the remaining cells. (It is also common to use CSV table format for input and output of marker locations, as shown in Figure-5.) The mask layout was configured with the chip patterns in a 4x2 array. The total number of measurements for the job shown equaled 125, with 15 specific features being investigated. CMi automatically generated the required simulated alignment images to be used for fine positioning of the AIMS tool. Alignment images can automatically be binned so that repeating structures can share a single alignment image to reduce file size requirements. During the validation phase, corresponding target CD values were extracted from the layout for each measurement position. The XML-based CMi output file was generated. The XML and simulated image results are shown in Figure-7. The corresponding photomask was loaded onto the AIMS fab193i for measurement acquisition. The CMi output file and an AIMS template recipe were combined to create a DFM job definition file. The lithographic settings used were 93NA and annular illumination setup. The measurement image sizes were approximately 20x20 microns mask-level. Each position was captured through-focus using 7 focal planes with a 1.6 micron mask-level step size. The DFM job was executed and the acquisition of the 125 images took 3 hours and 55 minutes. At roughly 32 measurement sites per hour, this equals a small reduction in the normal throughput rate of the AIMS fab193i of 40 sites per hour. The decrease is due to the pre-acquisition alignment process that occurs to increase measurement reliability. Analysis of the DFM job image file was executed using the AIMS Job Image Analysis wizard. An intensity threshold was determined using a sample of the acquired images (Figure-9). The Linewidth vs. Threshold plot was used to find a threshold that corresponded to the target CD value. Several positions were evaluated so that an average threshold could be calculated by the wizard software. Once this step was completed, the results of all measurement positions were calculated using the averaged threshold. These results were available in table format. Specific groups of features were available to be selected and plotted in CD and NILS distribution plots (Figure-10). Total analysis time from the point of loading the image file to the point of having the result table generated was 7 minutes. In contrast, manually analyzing 125 images would roughly take 4 or more hours. The substantial increase in analysis efficiency more than offsets the slight decrease in measurement throughput. Once the result table is generated, the user has the ability to sort and display the data in many ways. Macro and micro CD variation can be examined to determine effects of various process changes. The results can also be compared to wafer-level resist CDU plots to determine the amount of photomask contribution to ACLV. By using the option to export the results in a text format, further analysis can be performed using other applications. By providing reliable measurement locations with alignment images, and eliminating much of the manual effort involved in the AIMS operation, we increased the observed system throughput by 40% (Figure-11). 4. CONCLUSION A production-ready solution has successfully developed and deployed to link the mask layout data to the AIMS metrology system. The application of Carl Zeiss and Mentor Graphics tools allows for a more detailed inspection of the mask lithographic performance with real-time feedback of results. A flexible software interface allows both patternand region-based analysis of sub-cell patterns. This new application has demonstrated a significant improvement in the efficiency of photomask mask quality control activities.

6 ACKNOWLEDGEMENTS The authors would like to thank all of the team members at Samsung, Carl Zeiss, and Mentor Graphics who helped to make this project successful. REFERENCES 1. Stine, et al, Simulating the Impact of Pattern-Dependent Poly-CD Variation on Circuit Performance, Proc. IEEE Vol. 11 #4, Transactions on Semiconductor Manufacturing, Hector, et al, Evaluation of the critical dimension control requirements in the ITRS using statistical simulation and error budgets, Proc. SPIE Vol. 5377, Optical Microlithography XVII, , Progler and Xiao, Critical evaluation of photomask needs for competing 65-nm node RET options, Proc. SPIE Vol. 5040, Optical Microlithography XVI, Poortinga, et al, Investigation of Hyper-NA Scanner Emulation for Photomask CDU Performance, Proc. European Mask and Lithography Conference, Zibold, et al, Advances with new AIMSfab193 2 nd generation: a system for the 65nm node including immersion, Proc. SPIE Vol. 5853, Photomask and Next Generation Lithography Mask Technology, Poortinga, et al, Improved prediction of Across Chip Linewidth Variation (ACLV) with photomask aerial image CD metrology, Proc. SPIE Vol. 6349, Photomask Technology, Lucas, et al, Reticle enhancement verification for the 65nm and 45nm nodes, Proc. SPIE 6156, 61560R, 2006 Mask Screen effect Pattern Slope Dependency A etch t Cr t Cr σna θ sinθ = M A I M S In te n si ty t h B etch MTT (nm, SEM Figure-1: Example of mask topology effects as one of the motivating factors for AIMS TM CD measurement

7 MTT (nm) CD measurement in OPC point AIMS ADI ACI AIMS y = x R 2-80 = ADI Figure-2: An example of the practical validity of AIMS TM CD measurement: Process targeting at critical locations removes small maskmaking bias, but leaves residual errors in other features. AIMS measurements correlate well with resist measurements across different mask locations. Mask layout & Reticle Mentor Graphics CMi selects from the design the critical structures AIMS DFM analysis package captures critical structures on the mask Mask judgment And Lithographic evaluation Judge center Automatic analysis determines CD and NILS values of the critical patterns Result file and DFM map is used for pattern characterization Figure-3: Metrology Process

8 Figure-4: Mask Layout with Markers Displayed Figure-5: CSV Table of Markers on Mask Figure-6: CMi and Close-up of Markers (optional input/output format) Figure-7: XML and Simulated Aerial Images Output from CMi

9 Figure-8: AIMS Emulated Aerial Images in multi stack MSM file Figure-9: AIMS DFM analysis

10 Figure-10: AIMS DFM results Figure-11: Improved CD Measurement Process