MetroLER measures pattern roughness and other stochastic effects from SEM images, providing:
- The most accurate unbiased pattern roughness measurements using patent-pending technology
- Measurement of contact holes, lines and spaces, and SADP processes
- Predictions and analysis of local CDU, CD error, pattern placement error, edge placement error and within-feature LWR
- Detection and analysis of defects and pattern failures such as bridges, breaks, missing contacts and merged contacts
- Numerous other features and benefits
MetroLER is a standalone application that runs on a PC with Windows 7 or Windows 10. Please contact us for pricing and availability.
Contact Hole Measurement and Analysis - New in Version 1.5
Measurement and analysis of contact hole features
Understanding the effects of stochastics on contact hole features is one of the most important issues for EUV process development today. MetroLER helps solve these issues by providing the most accurate measurement of critical metrics such as local CDU, pattern placement error and edge placement error as well as the fraction of merged and missing contacts. For statistical accuracy, MetroLER can measure and analyze thousands of SEM images and more than a million contact hole features in a single batch. MetroLER also determines and removes the systematic errors in the SEM that can cause across-SEM field CD and pitch variation.
Unsurpassed Accuracy and Repeatability
The most accurate roughness measurements
MetroLER is the most accurate tool for measuring pattern roughness. The software includes:
No user-defined parameters
Unlike other tools, MetroLER is self-calibrating and requires no user-defined input parameters. This means that you will receive the same accurate measurements regardless of who is performing the analysis. In addition, all measurements and analyses are completely repeatable.
Accurate measurements across metrology tool settings
In the past, LER and LWR measurements have been known to vary depending on the SEM settings such as pixel size and the number of integration frames. MetroLER removes these measurement effects so the unbiased LER and LWR results are the same over a much wider range of metrology tool settings. With MetroLER you can determine the true roughness of your wafer patterns regardless of the measurement conditions. By subtracting the influence of the SEM, MetroLER gives a better measurement of what's really on the wafer.
Accurate measurements across wafer process conditions
The results from other roughness measurement tools are affected by process conditions making it difficult to determine the optimal conditions which minimize roughness. MetroLER does not have this issue. For example, when comparing post-litho data to post-etch data, you can trust MetroLER to provide the true measurements of the pattern roughness on the wafer across all data sets.
Other Key Capabilities
Detection and analysis of bridges and breaks as well as missing and merged contacts
MetroLER automatically detects bridges and breaks for line/space patterns as well as missing and merged contacts. Based on user-defined criteria, the software outputs an image that identifies the location of the defects, a table of the sizes and locations of the bridges and breaks, as well as statistics related to the defects.
Measurement of SADP and other double patterning processes
In addition to single patterning and EUV processes, MetroLER also analyzes images from SADP or other double patterning processes. A SEM image from a double patterning process includes two populations of line/space patterns that must be analyzed independently. MetroLER includes the capability to automatically distinguish between these two populations across multiple images, combine data from these images, then measure, analyze and report results of each set independently. The outputs for each population include CD, pitch, LER, LWR, PPR as well as many others.
Robust edge detection without filtering or smoothing
It is difficult to accurately and robustly detect edges in a noisy SEM image. That is why most SEM edge detection algorithms perform some level of smoothing or filtering (such as a Gaussian filter) to reduce the noise and make edge detection more reliable. But filtering smoothes out more than just the noise – it smoothes the rough edges of the feature as well. Thus, there is a suboptimal trade-off: robust edge detection produces biased answers.
MetroLER uses a different solution: the patent-pending Fractilia Inverse Linescan Model (FILM™). The FILM is a physics-based algorithm for edge detection that rejects noise without resorting to statistical filtering. The result is robust edge detection without modifying the true roughness characteristics of the feature edge you are detecting.
Accurate unbiased roughness measurements by subtracting out SEM errors
The noise in an SEM image adds to the roughness of the feature to produce an inflated, biased roughness measurement. MetroLER fixes this problem by measuring the noise in an SEM image and then statistically subtracting it out to produce a far more accurate and unbiased measurement. Among other benefits, this allows measurement tool matching when different tools have different levels of noise.
Another SEM error that affects roughness measurement is SEM image field distortion. Even sub-nanometer levels of field distortion over a micron field of view can cause significant increases in the measured low-frequency roughness. MetroLER provides features to help characterize and eventually subtract out systematic field distortion.
Prediction of the roughness impact on device features
Roughness measurement and characterization is based on the measurement of very long (typically about 2 μm) lines. But real device features are generally much shorter. These shorter device features suffer from two different aspects of stochastic-induced roughness: within-feature roughness and feature-to-feature roughness (called local CD uniformity, or LCDU). The Conservation of Roughness principle says the 3-sigma roughness of the very long line will be divided up between within-feature roughness and feature-to-feature roughness depending on a second important parameter of roughness: the correlation length.
MetroLER uses frequency analysis to provide not just an unbiased 3-sigma roughness measurement, but also an additional measurement of the correlation length. With this, MetroLER provides predictions of the stochastic behavior of device features as well.
Design of Experiments (DoE) capability
MetroLER allows users to compare and investigate roughness metrics as a function of one or two process parameters. To analyze DoE's such as a focus-exposure matrix the user can view and analyze the results from within MetroLER with no need to export to third party products.
Tested on thousands of images
MetroLER has been tested on thousands of customer images from EUV and 193 immersion processes. The results have proven the accuracy and consistency of the models, algorithms and methodologies used in MetroLER.
Software designed with semiconductor manufacturing in mind
Measurements in a semiconductor environment are used for understanding the behavior of a tool or process, optimizing that behavior, monitoring it, and eventually controlling it. MetroLER has been designed from the ground up to satisfy these multiple needs. It can serve as an engineering tool in the early days of discovery and process development, and as a manufacturing tool for process monitoring and control.
Understanding stochastic-induced roughness
Feature sizes continue to shrink faster than the roughness of their edges, so that the problem of roughness is growing significantly. Today’s best performance is not good enough for the next process technology node. Reduction of the negative consequences of stochastic-induced roughness requires a better understanding of the fundamental principles of stochastic-induced variation in lithography and etch. And better understanding begins with better metrology. Only by seeing more clearly what is happening in our patterning processes can we hope to understand what can, and can’t, be done to make things better.
Predicting the impact of stochastic-induced roughness on device features
What level of LWR is tolerable? How should stochastic effects be included in my overlay budget? How will stochastic-induced LCDU affect CD control in the upcoming process node? MetroLER, through its unbiased measurement of the full range of roughness metrics through frequency, can help to answer all of these questions, and more.
Process monitoring and control in manufacturing
As roughness effects grow in proportion to feature size, so does the need to monitor and control roughness in manufacturing. Today’s biased and limited roughness metrics provide too little information to properly monitor and control roughness at the 10-nm node and below. MetroLER subtracts out metrology tool effects and provides the needed accuracy and stability of the metrics so that production monitoring has the sensitivity and reliability needed.
Metrology tool optimization and tool matching
Metrology tool recipe optimization and tool matching are common tasks of the CD-SEM metrologist. But roughness measurements add additional challenges since SEM errors that have little or no impact on the measurement of mean CD can have a major impact on roughness measurement. The variation of SEM noise and SEM image field distortion over time and from tool to tool could have a significant impact on process monitoring of roughness metrics. MetroLER has the tools needed to measure, monitor, and subtract out these SEM measurement artifacts.
Resist characterization and optimization
Resist manufacturers must optimize their formulations with many goals in mind: resolution, process window, dose sensitivity, and low roughness. MetroLER gives resist manufacturers the information they need to perform through-dose, focus-exposure matrix, or formulation design of experiments with improved accuracy and trust.
Process and Tool Characterization
How does roughness change from before to after etch? In a complex SADP process, what steps need more care to keep roughness low? What hard mask materials provide lowest final roughness? Are various post-lithography smoothing techniques effective? How does a smoothing process step affect in-device features rather than just long line/space features? Use MetroLER to help answer these questions, and more.