Current state-of-the-art OPC (optical proximity correction) for 2-dimensional features consists of optimized fragmentation followed by site simulation and subsequent iterations to adjust fragment locations and minimize edge placement error (EPE). Internal and external constraints have historically been available in production quality code to limit the movement of certain fragments, and this provides additional control for OPC. Values for these constraints are left to engineering judgment, and can be based on lithography process limitations, mask house process limitations, or mask house inspection limitations. Often times mask house inspection limitations are used to define these constraints. However, these inspection restrictions are generally more complex than the 2 degrees of freedom provided in existing standard OPC software. Ideally, the most accurate and robust OPC software would match the movement constraints to the defect inspection requirements, as this prevents over-constraining the OPC solution.This work demonstrates significantly improved 2-D OPC correction results based on matching movement constraints to inspection limitations. Improvements are demonstrated on a created array of 2D designs as well as critical level chip designs used in 45nm technology. Enhancements to OPC efficacy are proven for several types of features. Improvements in overall EPE (edge placement error) are demonstrated for several different types of structures, including mushroom type landing pads, iso crosses, and H-bar structures. Reductions in corner rounding are evident for several 2-dimensional structures, and are shown with dense print image simulations. Dense arrays (SRAM) processed with the new constraints receive better overall corrections and convergence. Furthermore, OPC and ORC (optical rules checking) simulations on full chip test sites with the advanced constraints have resulted in tighter EPE distributions, and overall improved printing to target.
The OPC treatment of aerial mage ripples (local variations in aerial contour relative to constant target edges) is one of the growing issues with very low-k1 lithography employing hard off-axis illumination. The maxima and minima points in the aerial image, if not optimally treated within the existing model based OPC methodologies, could induce severe necking or bridging in the printed layout. The current fragmentation schemes and the subsequent site simulations are rule-based, and hence not optimized according to the aerial image profile at key points. The authors are primarily exploring more automated software methods to detect the location of the ripple peaks as well as implementing a simplified implementation strategy that is less costly. We define this to be an adaptive site placement methodology based on aerial image ripples.Recently, the phenomenon of aerial image ripples was considered within the analysis of the lithography process for cutting-edge technologies such as chromeless phase shifting masks and strong off-axis illumination approaches [3,4]. Effort is spent during the process development of conventional model-based OPC with the mere goal of locating these troublesome points. This process leads to longer development cycles and so far only partial success was reported in suppressing them (the causality of ripple occurrence has not yet fully been explored). We present here our success in the implementation of a more flexible model-based OPC solution that will dynamically locate these ripples based on the local aerial image profile nearby the features edges. This model-based dynamic tracking of ripples will cut down some time in the OPC code development phase and avoid specifying some rule-based recipes. Our implementation will include classification of the ripples bumps within one edge and the allocation of different weights in the OPC solution. This results in a new strategy of adapting site locations and OPC shifts of edge fragments to avoid any aggressive correction that may lead to increasing the ripples or propagating them to a new location. More advanced adaptation will be the ripples-aware fragmentation as a second control knob, beside the automated site placement.
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