Layout optimization for multilayer overlay targets

L. A. Binns; N. P. Smith; C. P. Ausschnitt; J. Morningstar; W. Muth; J. Schneider; R. Yerdon

doi:10.1117/12.659060

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14 March 2006 Layout optimization for multilayer overlay targets

L. A. Binns, N. P. Smith, C. P. Ausschnitt, J. Morningstar, W. Muth, J. Schneider, R. Yerdon

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Proceedings Volume 6155, Data Analysis and Modeling for Process Control III; 61550F (2006) https://doi.org/10.1117/12.659060
Event: SPIE 31st International Symposium on Advanced Lithography, 2006, San Jose, California, United States

Abstract

A novel overlay target developed by IBM and Accent Optical Technologies, Blossom, allows simultaneous overlay measurements of multiple layers (currently, up to 28) with a single target. This is achieved by a rotationally symmetric arrangement of small (4 micron) targets in a 50 micron square area, described more fully in a separate paper. In this paper, we examine the lessons learned in developing and testing the Blossom design. We start by examining proximity effects; the spacing of adjacent targets means that both the precision-like Total Measurement Uncertainty (TMU) and accuracy of a measurement can be affected by proximity of features. We use a mixture of real and modelled data to illustrate this problem, and find that the layout of Blossom reduces the proximity-induced bias. However, we do find that in certain cases proximity effects can increase the TMU of a particular measurement. The solution is to ensure that parts of the target that interact detrimentally are maximally separated. We present a solution to this, viewing the problem as a constrained Travelling Salesman Problem. We have imposed some global constraints, for example printing front-end and back-end layers on separate targets, and consistency with the overlay measurement strategy. Initially, we assume that pairwise measurements are either critical or non-critical, and optimize the layout so that the critical layers are both not placed adjacent to any prior or intermediate-layer features. We then build upon this structure, to consider the effect of low-energy implants (that cannot be seen once processed) and site re-use possibilities. Beyond this, we also investigate the impact of more strategic optimizations, for example, tuning the number of features on each layer. In each case, we present on-product performance data achieved, and modelled data on some additional target variants / extreme cases.

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L. A. Binns, N. P. Smith, C. P. Ausschnitt, J. Morningstar, W. Muth, J. Schneider, and R. Yerdon "Layout optimization for multilayer overlay targets", Proc. SPIE 6155, Data Analysis and Modeling for Process Control III, 61550F (14 March 2006); https://doi.org/10.1117/12.659060

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