Overlay accuracy calibration

Abstract

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.

Image-based overlay target design using a grating intersection

“…Random noise causes overlay inaccuracy, and various studies have been conducted to improve it. [10][11][12] There are also several research studies for evaluating and minimizing target noise. [13][14][15] This paper proposes a new overlay target to solve these two problems.…”

Device-correlated metrology for overlay measurements

“…Even for the same OVL target, different OVL values can be reported by different measurement conditions or different targets. [1][2][3][4][5][6] For example, different color filter selection can report different OVL values, specifically when the OVL target exhibits asymmetry (for either current or previous layer). Therefore, measurement conditions which are defined in the measurement recipe need to be optimized to provide the correct OVL.…”

“…Another way to evaluate the accuracy is the Qmerit algorithm, described last year and this year in SPIE papers. [1][2][3][4] This algorithm produces a quantitative measure of the asymmetry impact. The Archer Self Calibration algorithm, described in a poster in this year's SPIE, 3 allows correcting for that asymmetry and improving the OVL accuracy.…”

“…[1][2][3][4] This algorithm produces a quantitative measure of the asymmetry impact. The Archer Self Calibration algorithm, described in a poster in this year's SPIE, 3 allows correcting for that asymmetry and improving the OVL accuracy. The OVL linearity arrays and programmed OVL shifts in different fields are used to verify the target sensitivity to OVL and have some correlation to accuracy, but they are not immune to systematic OVL shifts.…”