MetroLER is the most accurate tool for measuring pattern roughness from scanning electron microscope images. 

MetroLER measures pattern roughness and other metrics from SEM images. MetroLER provides:

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MetroLER Capabilities


MetroLER outputs unbiased roughness measurements, the most accurate results available, as well as the impact of roughness on device features.

The most accurate roughness measurements

MetroLER is the most accurate tool for measuring pattern roughness. The software includes:

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.

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 lines/space patterns that must be analyzed independently. MetroLER includes the capability to automatically distinguish between these two populations, combine data from multiple 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.

Detection and analysis of bridges and breaks

MetroLER automatically detects bridges and breaks in SEM images 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.

MetroLER's unbiased measurements are consistently accurate down to 8 frames on this data set. Investigation and all SEM images in collaboration with imec.

MetroLER's unbiased measurements provide consistent accuracy across a range of metrology conditions. Investigation and all SEM images in collaboration with imec.

In addition to single patterning and EUV processes, MetroLER also accurately measures and analyzes SADP images and outputs results for each CD population.

MetroLER automatically detects and analyzes bridges and breaks when found on SEM images.



MetroLER Features


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, one is faced with an unpleasant 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, we provide 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.

The patent-pending Fractilia Inverse Linescan Model (FILM) detects edges without filtering or smoothing, resulting in significantly more accurate roughness measurements.

MetroLER automatically subtracts out SEM noise and SEM field distortion, resulting in extremely accurate unbiased roughness measurements.

MetroLER outputs numerous important roughness metrics.

MetroLER calculates and outputs a wide range of metrics regarding the impact of roughness on device features.

The Design of Experiments capability allows analysis of results as a function of up to two variables such as dose and focus. Investigation and all SEM images in collaboration with imec.



MetroLER Benefits



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.