OPC model error study through mask and SEM measurement error

International audienceMask and metrology errors such as SEM (Scanning Electron Microscopy) measurement errors are currently not accounted for when calibrating OPC models. Nevertheless, they can lead to erroneous model parameters therefore causing inaccuracies in the model prediction if these errors are of the same order of magnitude than targeted modeling accuracy. In this study, we used a dedicated design of hundreds of features exposed through a Focus Exposure Matrix (FEM). We measured the mask bias from target for these structures and investigated its impact on the model accuracy. For the metrology error, we compared the SEM measurements to AFM measurements for as much as 105 features exposed in various process conditions of dose and defocus. These data have then been used in a OPC model calibration procedure. We show that the impact of the metrology error is not negligible and demonstrate the importance of taking into account these errors in order to improve the reliability of the OPC models

Outliers detection by fuzzy classification method for model building

International audienceOptical Proximity Correction (OPC) is used in lithography to increase the achievable resolution and pattern transfer fidelity for IC manufacturing. Nowadays, immersion lithography scanners are reaching the limits of optical resolution leading to more and more constraints on OPC models in terms of simulation reliability. The detection of outliers coming from SEM measurements is key in OPC [1]. Indeed, the model reliability is based in a large part on those measurements accuracy and reliability as they belong to the set of data used to calibrate the model. Many approaches were developed for outlier detection by studying the data and their residual errors, using linear or nonlinear regression and standard deviation as a metric [8]. In this paper, we will present a statistical approach for detection of outlier measurements. This approach consists of scanning Critical Dimension (CD) measurements by process conditions using a statistical method based on fuzzy CMean clustering and the used of a covariant distance for checking aberrant values cluster by cluster. We propose to use the Mahalanobis distance [2] in order to improve the discrimination of the outliers when quantifying the similarity within each cluster of the data set. This fuzzy classification method was applied on the SEM CD data collected for the Active layer of a 65 nm half pitch technology. The measurements were acquired through a process window of 25 (dose, defocus) conditions. We were able to detect automatically 15 potential outliers in a data distribution as large as 1500 different CD measurement. We will discuss about these results as well as the advantages and drawbacks of this technique as automatic outliers detection for large data distribution cleaning

High Accuracy 65nm OPC Verification: Full Process Window Model vs. Critical Failure ORC

It is becoming more and more difficult to ensure robust patterning after OPC due to the continuous reduction of layout dimensions and diminishing process windows associated with each successive lithographic generation. Lithographers must guarantee high imaging fidelity throughout the entire range of normal process variations. The techniques of Mask Rule Checking (MRC) and Optical Rule Checking (ORC) have become mandatory tools for ensuring that OPC delivers robust patterning. However the first method relies on geometrical checks and the second one is based on a model built at best process conditions. Thus those techniques do not have the ability to address all potential printing errors throughout the process window (PW). To address this issue, a technique known as Critical Failure ORC (CFORC) was introduced that uses optical parameters from aerial image simulations. In CFORC, a numerical model is used to correlate these optical parameters with experimental data taken throughout the process window to predict printing errors. This method has proven its efficiency for detecting potential printing issues through the entire process window [1]. However this analytical method is based on optical parameters extracted via an optical model built at single process conditions. It is reasonable to expect that a verification method involving optical models built from several points throughout PW would provide more accurate predictions of printing errors for complex features. To verify this approach, compact optical models similar to those used for standard OPC were built and calibrated with experimental data measured at the PW limits. This model is then applied to various test patterns to predict potential printing errors. In this paper, a comparison between these two approaches is presented for the poly layer at 65 nm node patterning. Examples of specific failure predictions obtained separately with the two techniques are compared with experimental results. The details of implementing these two techniques on full product layouts are also included in this study

Process Window OPC Verification: Dry versus Immersion Lithography for the 65 nm node

Ensuring robust patterning after OPC is becoming more and more difficult due to the continuous reduction of layout dimensions and diminishing process windows associated with each successive lithographic generation. Lithographers must guarantee high imaging fidelity throughout the entire range of normal process variations. To verify the printability of a design across process window, compact optical models similar to those used for standard OPC are used. These models are calibrated from experimental data measured at the limits of the process window. They are then applied to the design to predict potential printing failures. This approach has been widely used for dry lithography. With the emergence of immersion lithography in production in the IC industry, the predictability of this approach has to be validated on this new lithographic process. In this paper, a comparison between the dry lithography process model and the immersion lithography process model is presented for the Poly layer at 65 nm node patterning. Examples of specific failure predictions obtained separately with the two processes are compared with experimental results. A comparison in terms of process performance will also be a part of this study

Through-process window resist modelling strategies for the 65 nm node

Ensuring robust patterning after OPC is becoming more and more difficult due to the continuous reduction of layout dimensions and diminishing process windows associated with each successive lithographic generation. Lithographers must guarantee high imaging fidelity throughout the entire range of normal process variations. As a result, post-OPC verification methods have become indispensable tools for avoiding pattern printing issues. The majority of these methods are primarily based on lithographic simulations of pattern printing behaviour across dose and focus variations. The models used for these simulations are compact optical models combined with one single resist model. Even if very predictive resist models exist, they have often a large number of parameters to fit and suffer from long computing times to execute the simulations. Simplified resist models are thus needed to enhance run-time computing during simulation. The objective of this study is to test the predictability of such resist models across the process window. Two different resist models will be considered in this study. The first resist model is a pure variable threshold resist model. The second resist modelling approach is a simplified physical model which uses Gaussian convolutions and a constant threshold to model resist printing behaviour. The study concentrates on poly layer patterning for the 65 nm node. Examples of specific simulations obtained with the two different techniques are compared against experimental results