Resolution enhancement technology: the past, the present, and extensions for the future

Schellenberg, F. M.

doi:10.1117/12.548923

Cited by 95 publications

(61 citation statements)

References 39 publications

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“…It is the most significant contributor towards chip leakage and timing variability. Although Optical Proximity Correction (OPC) techniques have been implemented in today's designs, the effects of these variations have not been completely eliminated [9]. ACLV due to pitch variation in 45nm process using λ=193nm illumination system for any layout is shown using Bossung plot.…”

Section: A Linewidth Estimationmentioning

confidence: 99%

Statistical timing analysis based on simulation of lithographic process

Sreedhar

Kundu

2009

2009 IEEE International Conference on Computer Design

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Abstract-The length of poly-gate printed on silicon depends on exposure dose, depth of focus, photo-resist thickness and planarity of the surface. In sub-wavelength lithography, polygate length also varies with layout topology. Poly-gate length determines the effective channel length of a transistor, which determines its performance. Since the sources of error are hard to control, statistical analysis can be used to measure the impact on circuit timing characteristics. Typical lithographyaware methodologies consider only systematic variation such as across chip linewidth variation (ACLV). In this paper we propose a statistical technique for timing yield prediction, based on variational lithography modeling of physical circuit layout. By statistically varying lithographic process parameters we estimate the difference in timing yield estimation of a design. Our simulation results show that if manufacturing process parameters follow a Gaussian distribution, resulting transistors follow a skewed normal distribution, where a greater number of them will have shorter channel length. This led us to investigate whether Statistical Static Timing Analysis (SSTA) is overly pessimistic. The baseline delay model assumed for SSTA in out approach is a Gaussian delay model fitted to skew normal distribution data obtained from statistical litho simulation. Our experiments showed that even after recentering Gaussian delay model to fit the channel length data with minimum error, it is still overly pessimistic and significantly underestimates circuit performance.

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Section: A Linewidth Estimationmentioning

confidence: 99%

Statistical timing analysis based on simulation of lithographic process

Sreedhar

Kundu

2009

2009 IEEE International Conference on Computer Design

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“…1 Several resolution enhancement techniques (RETs) have been developed to improve the performance of optical lithography. [1][2][3] Optical proximity correction (OPC) is one of these RETs. 4 Its objective is synthesizing an input mask to deliver a desired output pattern.…”

Section: Introductionmentioning

confidence: 99%

Mask-filtering-based inverse lithography

Xia

Liu

2013

J. Micro/Nanolith. MEMS MOEMS

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Abstract. We propose a new regularization framework for inverse lithography that regularizes masks directly by applying a mask filtering technique to improve computational efficiency and to enhance mask manufacturability. This technique is different from the conventional regularization method that regularizes a mask by incorporating various penalty functions to the cost function. We design a specific mask filter for this purpose. Moreover, we introduce a metric called edge distance error (EDE) to guide mask synthesis and establish the correlation between pattern error and edge placement error (EPE) via EDE. We prove that EDE has the same dimension as EPE and has a continuous expression as pattern error. Simulation results demonstrating the validity and efficiency of the proposed method are presented. © 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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“…[1][2][3][4][5] Resolution in optical lithography obeys the Rayleigh resolution limit R ¼ k 1 ðλ∕NAÞ, where λ is the wavelength, NA is the numerical aperture, and k 1 is the process constant which can be minimized through RET methods. [6][7][8][9][10][11][12][13][14][15] Optical proximity correction (OPC) is one of the key RETs that modifies the mask pattern to precompensate for imaging distortions.…”

Section: Introductionmentioning

confidence: 99%

Fast pixel-based optical proximity correction based on nonparametric kernel regression

Song

et al. 2014

J. Micro/Nanolith. MEMS MOEMS

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Abstract. Optical proximity correction (OPC) is a resolution enhancement technique extensively used in the semiconductor industry to improve the resolution and pattern fidelity of optical lithography. In pixel-based OPC (PBOPC), the layout is divided into small pixels, which are then iteratively modified until the simulated print image on the wafer matches the desired pattern. However, the increasing complexity and size of modern integrated circuits make PBOPC techniques quite computationally intensive. This paper focuses on developing a practical and efficient PBOPC algorithm based on a nonparametric kernel regression, a well-known technique in machine learning. Specifically, we estimate the OPC patterns based on the geometric characteristics of the original layout corresponding to the same region and a series of training examples. Experimental results on metal layers show that our proposed approach significantly improves the speed of a current professional PBOPC software by a factor of 2 to 3, and may further reduce the mask complexity. © 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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Resolution enhancement technology: the past, the present, and extensions for the future

Cited by 95 publications

References 39 publications

Statistical timing analysis based on simulation of lithographic process

Statistical timing analysis based on simulation of lithographic process

Mask-filtering-based inverse lithography

Fast pixel-based optical proximity correction based on nonparametric kernel regression

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