Phenomena and OPC solution of ripple patterns for 65-nm node

Lai, Chih‐Ming; Ho, Jeng-Shiun; Lai, Chien-Wen; Tsai, Cheng‐Kun; Tsay, Cherng-Shyan; Chen, Jeng-Horng; Liu, Ru-Gun; Ku, Y.H.; Lin, B. N.

doi:10.1117/12.544250

Cited by 3 publications

(3 citation statements)

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“…Ideally, the image contour of a straight edge is expected to be uniform and straight, if there is no inter-feature effect from adjacent polygons. Off-axis illumination can introduce nonlinear effects into a straight line due to the proximity effects inherent in the illumination [2]. This presented work does not aim to fully explain the ripple causality.…”

Section: -Lmentioning

confidence: 79%

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Implementation of adaptive site optimization in model-based OPC for minimizing ripples

et al. 2006

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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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Section: -Lmentioning

confidence: 79%

“…According to the previous papers [1,2], the ripple locations and frequency are correlated to the illumination parameters and also the feature patterns and pitches. Derivation of the rules for site placement during process and recipe development can consume significant time and resources.…”

Section: Aerial Image Ripples Detectionmentioning

confidence: 99%

Implementation of adaptive site optimization in model-based OPC for minimizing ripples

et al. 2006

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“…In recent years, the impact of aerial image ripples on pattern fidelity has been highlighted [1] [2]. The ripple phenomenon is highly pattern and illumination dependent, with the worst effects seen under the following conditions:…”

Section: Introductionmentioning

confidence: 99%

Enhanced model-based OPC for 65 nm and below

Word

Cobb

2004

SPIE Proceedings

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Current model based OPC software operates under a set of simple guiding principles. First, a design is fragmented into finitely sized segments, the sizes and numbers of which are limited by run-time and mask constraints. Within each fragment the intensity (aerial image) and edge-placement error (EPE) are calculated at a single location. Finally, the length of the entire fragment is moved to correct for the EPE at that location. Although the computation of intensity and EPE are "model based", the fragmentation and simulation site placement are typically "rules based". Problems with this methodology can arise whenever the location of the fragments and simulation points are non-optimal. This can be of particular concern with very low-k1 lithography employing hard off-axis illumination, where aerial image "ripples" are a known issue. The authors will propose several solutions to these issues involving model based optimization and placement of fragments and simulation sites.

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Ripple-aware optical proximity correction fragmentation for back-end-of-line designs

Wang

Wilkinson

2018

J. Micro/Nanolith. MEMS MOEMS

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Phenomena and OPC solution of ripple patterns for 65-nm node

Cited by 3 publications

References 0 publications

Implementation of adaptive site optimization in model-based OPC for minimizing ripples

Implementation of adaptive site optimization in model-based OPC for minimizing ripples

Enhanced model-based OPC for 65 nm and below

Ripple-aware optical proximity correction fragmentation for back-end-of-line designs

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