A standalone alignment technology was developed as a fundamental solution to improve on-product overlay (OPO). It enables high performance alignment measurements, and delivers state-of-the-art feed forward corrections to exposure scanner. Dense alignment sampling and high-order field distortion correction is effective for scanner fingerprint matching and for heat related field distortions. A modeling and sampling optimization software is a powerful tool for dense sampling and high-order overlay correction with minimal throughput loss. We performed an overlay experiment using the standalone alignment technology coupled with a modeling and sampling optimization software, which demonstrates on-product overlay improvement potential for next generation manufacturing accuracy and productivity challenges.
In leading edge lithography, overlay is usually controlled by feedback based on measurements on overlay targets, which are located between the dies. These measurements are done directly after developing the wafer. However, it is well-known that the measurement on the overlay marks does not always represent the actual device overlay correctly. This can be due to different factors, including mask writing errors, target-to-device differences and non-litho processing effects, for instance by the etch process.1
In order to verify these differences, overlay measurements are regularly done after the final etch process. These post-etch overlay measurements can be performed by using the same overlay targets used in post-litho overlay measurement or other targets. Alternatively, they can be in-device measurements using electron beam measurement tools (for instance CD-SEM). The difference is calculated between the standard post-litho measurement and the post-etch measurement. The calculation result is known as litho-etch overlay bias.
This study focuses on the feasibility of post-etch overlay measurement run-to-run (R2R) feedback instead of post-lithography R2R feedback correction. It is known that the post-litho processes have strong non-linear influences on the in-device overlay signature and, hence, on the final overlay budget. A post-etch based R2R correction is able to mitigate such influences.2
This paper addresses several questions and challenges related to post-etch overlay measurement with respect to R2R feedback control. The behavior of the overlay targets in the scribe-line is compared to the overlay behavior of device structures. The influence of different measurement methodologies (optical image-based overlay vs. electron microscope overlay measurement) was evaluated. Scribe-line standard overlay targets will be measured with electron microscope measurement. In addition, the influence of the intra-field location of the targets on device-to-target shifts was evaluated.
It was proven that higher order intra-field alignment data modeling and correction has the potential to improve overlay performance by correcting reticle heating and lens heating effects intra-wafer and wafer- to-wafer.1 But there were also challenges shown that needed further investigation. As the alignment measurement is done on a coordinate system with absolute positions, the modeled iHOPC values might be high. A suitable method needs to be developed to distinguish between tool-to-tool offsets, process influence and layer-to-layer tool stack effect. In this paper we will take the next step and evaluate the overlay improvement potential by using intra-field alignment data in an overlay feed-forward simulation. An overlay run-to-run simulation is afterwards performed to estimate the optimization potential. To simulate higher order intra-field overlay, dense alignment data is needed. Facing the challenge of optimizing the number of measured marks but not losing relevant information, an intra-field alignment mark sampling optimization is done to find the best compromise between throughput and overlay accuracy.
Before each wafer exposure, the photo lithography scanner’s alignment system measures alignment marks to correct for placement errors and wafer deformation. To minimize throughput impact, the number of alignment measurements is limited. Usually, the wafer alignment does not correct for intrafield effects. However, after calibration of lens and reticle heating, residual heating effects remain. A set of wafers is exposed with special reticles containing many alignment marks, enabling intra-field alignment. Reticles with a dense alignment layout have been used, with different defined intra-field bias. In addition, overlay simulations are performed with dedicated higher order intra-field overlay models to compensate for wafer-to-wafer and across-wafer heating.
Overlay errors between two layers can be caused by non-lithography processes. While these errors can be compensated by the run-to-run system, such process and tool signatures are not always stable. In order to monitor the impact of non-lithography context on overlay at regular intervals, a systematic approach is needed. Using various machine learning techniques, significant context parameters that relate to deviating overlay signatures are automatically identified. Once the most influential context parameters are found, a run-to-run simulation is performed to see how much improvement can be obtained. The resulting analysis shows good potential for reducing the influence of hidden context parameters on overlay performance. Non-lithographic contexts are significant contributors, and their automatic detection and classification will enable the overlay roadmap, given the corresponding control capabilities.
Non-linear overlay deformation is a well-known problem in critical lithography steps. A significant root cause is nonuniform stress, often caused by high temperature processes. Non-uniform stress in the wafer causes vertical deformation of the wafer, which can be measured by topography measurement equipment. In this case study, clustering is done on the topography data to sort each wafer into groups. Using the context information from the clustering, overlay feedback is computed on a wafer level basis. The evaluation of the approach is done with a run-to-run simulation, which allows optimization of this method and evaluation of the on-product overlay performance improvement. In the analysis, different wafer zones are distinguished to characterize the improvement potential for the different zones.
This paper focuses on orthogonal model corrections where model parameters do not influence each other as long as the
measurement layout is sufficiently symmetric. For the grid correction we used Zernike polynomials, and for the intrafield
correction we used a two-dimensional set of Legendre polynomials. We enabled these corrections by developing a
transformation matrix as an exposure tool is incapable of correcting such orthogonal polynomials. Simulation with
OVALiS shows that the linear parameters get stabilized by several factors when using a combined Zernike/Legendre
model. The correlation between linear and higher order parameters disappears, and overlay mean plus 3-sigma improves
up to ~15–20%. Simulated data agrees well with experimental and electrical data. Additionally, we introduced an
interpolated metric that probed the wafer and field with a dense grid. This interpolated metric showed that the
Zernike/Legendre model-based correction does not cause over-correction like that seen on standard polynomial models.
We have tested higher order process corrections comprehensively by enabling an orthogonal model, as well as by
making use of interpolated metrics to monitor the overlay performance. These orthogonal models can be implemented in
the production line based on inline overlay data where interpolated metrics will ensure that there is no over-correction
and no negative impact on product.