Enhanced model-based OPC for 65 nm and below

Word, James; Cobb, Nicolas B.

doi:10.1117/12.568757

Cited by 11 publications

(10 citation statements)

References 3 publications

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“…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%

“…In previous published papers, some practical rule-based or table-driven solutions were proposed and successfully contributed in reducing the ripples and suppressing their destructive effects. [1] In our paper, we present some more automated software methods that will lead to a true model-based site placement, which we call "adaptive site" optimization. The methods applied here offer promise for addressing other challenges of current OPC software and will be an area of future research…”

Section: -Lmentioning

confidence: 99%

“…This issue clearly appeared in the cutting-edge technology nodes (65 nm and below) with very low-k1 lithography and hard off-axis illumination (OAI). One example of that is applying attenuated phase-shift mask OPC (Att-OPC) and dipole/quadrupole illumination [1].…”

Section: -Lmentioning

confidence: 99%

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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: Aerial Image Ripples Detectionmentioning

confidence: 99%

Section: -Lmentioning

confidence: 99%

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

et al. 2006

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“…Therefore, the main differences for OPC/RET expected with the transition to the 45nm device generation are the need for models with improved accuracy and functionality; for new model types (to replace current rules [8] or manual effort); and for higher productivity production OPC. Higher productivity OPC (and verification) software and systems are beyond the scope of this paper; however, we should certainly discuss further the increased modeling requirements.…”

Section: Future Verification Needsmentioning

confidence: 99%

Reticle enhancement verification for the 65nm and 45nm nodes

Lucas¹,

Patterson²,

Boone³

et al. 2006

SPIE Proceedings

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In the last 2 years, the semiconductor industry has recognized the critical importance of verification for optical proximity correction (OPC) and reticle/resolution enhancement technology (RET). Consequently, RET verification usage has increased and improved dramatically. These changes are due to the arrival of new verification tools, new companies, new requirements and new awareness by product groups about the necessity of RET verification. Currently, as the 65nm device generation comes into full production and the 45nm generation starts full development, companies now have the tools and experience (i.e., long lists of previous errors to avoid) needed to perform a detailed analysis of what is required for 45nm and 65nm RET verification. In previous work [1] we performed a theoretical analysis of OPC & RET verification requirements for the 65nm and 45nm device generations and drew conclusions for the ideal verification strategy. In this paper, we extend the previous work to include actual observed verification issues and experimental results. We analyze the historical experimental issues with regard to cause, impact and optimum verification detection strategy. The results of this experimental analysis are compared to the theoretical results, with differences and agreement noted. Finally, we use theoretical and experimental results to propose an optimized RET verification strategy to meet the user requirements of 45nm development and the differing requirements of 65nm volume production.

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“…Source optimization is mainly aimed at altering the source shape, 1,2 whereas mask optimization is aimed at modulating the amplitude of the electric field to compensate for the optical proximity effect. 3,4 In 2002, Rosenbluth et al proposed the first source mask optimization (SMO) method that exploits the synergy between the source and mask to achieve a higher resolution. 5 Since then, a number of SMO methods have been proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

Parametric source-mask-numerical aperture co-optimization for immersion lithography

Guo

Dong

et al. 2014

J. Micro/Nanolith. MEMS MOEMS

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Abstract. Source mask optimization (SMO) is a leading resolution enhancement technique in immersion lithography at the 45-nm node and beyond. Current SMO approaches, however, fix the numerical aperture (NA), which has a strong impact on the depth of focus (DOF). A higher NA could realize a higher resolution but reduce the DOF; it is very important to balance the requirements of NA between resolution and the DOF. In addition, current SMO methods usually result in complicated source and mask patterns that are expensive or difficult to fabricate. This paper proposes a parametric source-mask-NA co-optimization (SMNO) method to improve the pattern fidelity, extend the DOF, and reduce the complexity of the source and mask. An analytic cost function is first composed based on an integrative vector imaging model, in which a differentiable function is applied to formulate the source and mask patterns. Then, the derivative of the cost function is deduced and a gradient-based algorithm is used to solve the SMNO problem. Simulation results show that the proposed SMNO can achieve the optimum combination of parametric source, mask, and NA to maintain high pattern fidelity within a large DOF. In addition, the complexities of the source and mask are effectively reduced after optimization. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Enhanced model-based OPC for 65 nm and below

Cited by 11 publications

References 3 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

Reticle enhancement verification for the 65nm and 45nm nodes

Parametric source-mask-numerical aperture co-optimization for immersion lithography

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