In order to fulfill the ever tightening requirements of advanced node overlay budgets, overlay metrology is becoming more and more sensitive to even the smallest imperfections in the metrology target. Under certain circumstances, inaccuracy due to such target imperfections can become the dominant contribution to the metrology uncertainty and cannot be quantified by the standard TMU contributors. In this paper we describe a calibration method that makes the overlay measurement robust to target imperfections without diminishing its sensitivity to the target overlay. The basic assumption of the method is that overlay measurement result can be approximated as the sum of two terms: the accurate overlay and the measurement inaccuracy (independently of the conventional contributors). While the first term (the “real overlay”) is robust it is known that the overlay target inaccuracy depends on the measurement conditions. This dependence on measurement conditions is used to estimate quantitative inaccuracy by means of the overlay quality merit which was described in previous publications. This paper includes the theoretical basis of the method as well as experimental validation.
The semiconductor industry is moving toward 20nm nodes and below. As the Overlay (OVL) budget is getting tighter at these advanced nodes, the importance in the accuracy in each nanometer of OVL error is critical. When process owners select OVL targets and methods for their process, they must do it wisely; otherwise the reported OVL could be inaccurate, resulting in yield loss. The same problem can occur when the target sampling map is chosen incorrectly, consisting of asymmetric targets that will cause biased correctable terms and a corrupted wafer. Total measurement uncertainty (TMU) is the main parameter that process owners use when choosing an OVL target per layer. Going towards the 20nm nodes and below, TMU will not be enough for accurate OVL control. KLA-Tencor has introduced a quality score named ‘Qmerit’ for its imaging based OVL (IBO) targets, which is obtained on the-fly for each OVL measurement point in X & Y. This Qmerit score will enable the process owners to select compatible targets which provide accurate OVL values for their process and thereby improve their yield. Together with K-T Analyzer’s ability to detect the symmetric targets across the wafer and within the field, the Archer tools will continue to provide an independent, reliable measurement of OVL error into the next advanced nodes, enabling fabs to manufacture devices that meet their tight OVL error budgets.
As the semiconductor industry advances to smaller design rules, Photoresist performance is critical for the
tight lithography process. Critical Dimension (CD), Side Wall Angle (SWA) and Photoresist height,
which are critical for the final semiconductor patterning, depend on the Photoresist chemistry. Each
Photoresist batch has to be qualified to verify that it can achieve the required quality specifications.
Photoresist qualification is done by exposing Photoresist and monitoring outcome after developing.
In this work, Archer 300LCM scatterometry-based Optical CD (OCD) was evaluated using Dow 193
Immersion Top Coat Free Photoresist and Anti Reflection Layers (ARL). As part of the sensitivity
analysis, changes in Photoresist thickness, ARL thickness and Photoresist formulation were evaluated.
Results were compared to CD-SEM measurements. The CD sensitivity was evaluated on two grating
dense line and space features with nominal Middle CD (MCD) values of 37nm and 75nm. Sensitivity of
the OCD for Photoresist parameters was demonstrated.
As overlay budget continues to shrink, an improved analysis of the different contributors to this budget is needed. A
major contributor that has never been quantified is the accuracy of the measurements. KLA-Tencor developed a quality
metric, that calculates and attaches an accuracy value to each OVL target. This operation is performed on the fly during
measurement and can be applied without affecting MAM time or throughput. Using a linearity array we demonstrate that
the quality metric identifies targets deviating from the intended OVL value, with no false alarms.
Controlling overlay performance has become one of the key lithographic challenges for advanced integrated circuit
manufacturing. Overlay error budgets of 4 nm in the 2x node require careful consideration of all potential error sources.
Overlay data modeling is a key component for reducing systematic wafer and field variation, and is typically based on
ordinary least squares (OLS) regression. OLS assumes that each data point provides equally reliable information about
the process variation. Weighted least squares (WLS) regression can be used to improve overlay modeling by giving
each data point an amount of influence on the model which depends on its quality. Here we use target quality merit
metrics from the overlay metrology tool to provide the regression weighting factors for improved overlay control in
Currently, the performance of overlay metrology is evaluated mainly based on random error contributions such as
precision and TIS variability. With the expected shrinkage of the overlay metrology budget to < 0.5nm, it becomes
crucial to include also systematic error contributions which affect the accuracy of the metrology. Here we discuss
fundamental aspects of overlay accuracy and a methodology to improve accuracy significantly.
We identify overlay mark imperfections and their interaction with the metrology technology, as the main source of
overlay inaccuracy. The most important type of mark imperfection is mark asymmetry. Overlay mark asymmetry leads
to a geometrical ambiguity in the definition of overlay, which can be ~1nm or less. It is shown theoretically and in
simulations that the metrology may enhance the effect of overlay mark asymmetry significantly and lead to metrology
inaccuracy ~10nm, much larger than the geometrical ambiguity. The analysis is carried out for two different overlay
metrology technologies: Imaging overlay and DBO (1st order diffraction based overlay). It is demonstrated that the
sensitivity of DBO to overlay mark asymmetry is larger than the sensitivity of imaging overlay.
Finally, we show that a recently developed measurement quality metric serves as a valuable tool for improving overlay
metrology accuracy. Simulation results demonstrate that the accuracy of imaging overlay can be improved significantly
by recipe setup optimized using the quality metric. We conclude that imaging overlay metrology, complemented by
appropriate use of measurement quality metric, results in optimal overlay accuracy.