Java Based Quantitative Imaging Tool

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Java Based Quantitative Imaging Tool: MetroScope for semiconductor data analysis

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Java Based Quantitative Imaging Tool

  1. 1. A Digital Image Processor for Complex Shape Metrology Qi-De Qian IC Scope Research, 159 Gilbert Ave. Santa Clara, CA 95051 qqian@icscope.com
  2. 2. IntroductionRecent advances in digital image processing and computer graphics are appliedto solve the complex shape metrology problem in IC and mask manufacturing.A quantitative image analyzer, MetroScope™ have been developed to meet thecomplex shape metrology needs arising from the semiconductor industry.MetroScope™ consists of an image based metrology engine, a digital photoalbum organization interface, and a web based image sharing framework. Thecapability for MetroScope™ to extract feature shape, measure arbitrary areaand line edge roughness are demonstrated. This system extends the metrologytool set by providing complex feature shape analysis capabilities. In maskmanufacturing, MetroScope™ can provide valuable new capabilities for taskssuch as: OPC feature characterization, defect metrology, and mask processingcapability evaluation.
  3. 3. Digital Photo Album for Data Images
  4. 4. MetroScope™ GUI ComponentsdigiAlbum image processor pixel trace analyzer
  5. 5. Intrusion/Protrusion Defects Scale bar• With the digital album in display, a raw image is loaded into the main image processor by a click on the thumbnail.• This image contains a defect that causes narrowing on part of the line.• We want to measure the size of this defect by – the amount of line width narrowing it causes, and – the total area of the defect. What is the size of this defect? The main image processor
  6. 6. Defect Size Measurement Pull down menu• Using the image processing Function panel functions, we can easily obtain the single pixel outline of the features.• Image processing functions are located on the pull down menu and the image processor function panel.• Line width in pixels are measured with a mouse, and the measurement positions are indicated on the image.• The actual line width in microns is obtained by scaling with the pixel count of the scale bar. Smaller CD as due to defect The main image processor
  7. 7. Defect Area Measurement Message box that• An alternative way of displays the results characterizing a defect is to calculate the defect area.• For intrusion/protrusion defects, the user needs to draw an assist line to isolate the defect.• The user calculates the area by clicking inside the area surrounded by the outline of the defect and the assist line.• In this example, we have reversed the color for easy viewing and printing. Defect area calculated in pixels
  8. 8. Defect Area Measurement Noise reduction Area = 1.27 um^2 160 160 140 Pixel Intensity 140Pixel Intensity 120 120 100 100 80 80 60 60 0 50 100 150 200 0 50 100 150 200 Pixel Index Pixel Index
  9. 9. Corner Rounding Measurement• Unlike the polygons on the IC Thumbnail of the Image after layout database, the actual original image preprocessing patterns on the chip have rounded corners.• Photomasks made with scanning laser beam or low energy e-beam technology all have significant corner rounding.• The figure on the right shows an SEM image being processed by MetroScope™ for subsequent corner rounding measurements. We want to measure the corner rounding of this line end.
  10. 10. Measure Corner Pull Back• One method to quantify corner Thumbnail of the Corner pull-back: rounding is to measure the pull original image 39.6 (enlarged) pixels back.• To do that, we first draw two assist lines that extend the two sides of the corner.• Pull back is measured from the intersection of the two assist lines to the tip of the corner.• For perfectly circular corners, the pull back is related to the radius by R=(Pull_Back)/(sqrt(2)-1) . Corner pull-back: 60.8 (enlarged) pixels
  11. 11. Corner Rounding by Missing Area Thumbnail of the Missing area: 2638• We can also measure corner original image (enlarged) pixels rounding in terms of missing area.• To do that, we simply calculate the area between the corner and the assist lines.• This method is often more useful for mask pattern fidelity analysis, since a stepper responds to area change when the area concerned is small. Missing area: 5350 (enlarged) pixels
  12. 12. Line Edge Roughness (LER)• Edge roughness are a major problem in the new 193nm or 157nm photoresist patterns.• The SEM picture on the right shows edge roughness in a line/space pattern.• MetroScope allows a user to extract the line edge and quantify its roughness as a standard deviation. Photoresist lines Spaces
  13. 13. LER Measurement Std. Dev. : 1.21 pixels (all edges)Std. dev. 1.427 1.14 1.25 0.94 1.16(pixels) 0.88 1.47 1.17 1.186 1.32
  14. 14. Summary• MetroScope™ offers a highly flexible solution to complex mask pattern metrology.• In mask manufacturing, MetroScope™ it ideal for tasks such as OPC characterization, defect metrology, and process capability evaluation.• We demonstrate the capability of MetroScope™ in – Defect area and dimension measurement – Corner rounding and pull back measurement – Line edge roughness measurement

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