OPC modeling and correction solutions for EUV lithography

Word, James; Zuniga, Christian; Lam, Michael; Habib, Mohamed A.; Adam, Kostas; Oliver, Michael

doi:10.1117/12.899591

Cited by 11 publications

(10 citation statements)

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“…These models are currently used in many EUV lithography simulators. [9][10][11] An implicit assumption of the pattern decomposition method in the models is that the mask pattern is large and isolated. However, optical proximity correction (OPC) masks are decorated by small patterns (serifs and assist features) and the pattern densities are high.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of CNN for fast EUV lithography simulation using iN3 logic mask patterns

Tanabe

Jinguji

Takahashi

2023

DTCO and Computational Patterning II

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Mask 3D effects distort diffraction amplitudes from EUV masks. In the previous work, we developed a CNN which predicted the distorted diffraction amplitudes very fast from input mask patterns. The mask patterns in the work were restricted to Manhattan patterns. In general, the accuracy of neural networks depends on their training data. The CNN trained by Manhattan patterns cannot be used to general mask patterns. However, our CNN architecture contains 70 M parameters, and the architecture itself could be applied to general mask patterns. In this work, we apply the same CNN architecture to mask patterns which mimic iN3 logic metal or via layers. Additionally, to study more general mask patterns, we train CNNs using iN3 metal/via patterns with OPC and curvilinear via patterns. In total we train five different CNNs: metal patterns w/wo OPC, via patterns w/wo OPC, and curvilinear via patterns. After the training, we validate each CNN using validation data with the above five different characteristics. When we use the training and validation data with same characteristics, the validation loss becomes very small. Our CNN architecture is flexible enough to be applied to iN3 metal and via layers. On the other hand, using the training and validation data with different characteristics will lead to large validation loss. The selection of training data is very important to obtain high accuracy. We examine the impact of mask 3D effects on iN3 metal layer. Large difference is observed in T2T CD calculated by thin mask model and thick mask model. This is due to the mask shadowing effect at T2T slits. Our CNN successfully predicts T2T CD of thick mask model, which is sensitive to the mask 3D effect.

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Section: Introductionmentioning

confidence: 99%

Evaluation of CNN for fast EUV lithography simulation using iN3 logic mask patterns

Tanabe

Jinguji

Takahashi

2023

DTCO and Computational Patterning II

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“…OPC-litho models for these tight pitch layers must comprehend full-field EUV effects such as non-telecentricity of the slit, flare, and black border [3]. OPC model errors need to scale with the edge placement requirements of each technology node.…”

Section: Optics and Resist Modellingmentioning

confidence: 99%

Direct print EUV patterning of tight pitch metal layers for Intel 18A process technology node

Venkatesan

Guven²,

Bhawe³

et al. 2022

International Conference on Extreme Ultraviolet Lithography 2022

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This paper describes the direct print Extreme Ultra Violet (EUV) technology used for lithographic patterning of ~30-36 nm pitch metal layers of Intel 18A technology node. Direct print EUV delivers cost effective pitch scaling to enable flexible design rules and ease of use for layout designers. Careful co-optimization of the illumination source, photoresist and lithography stack is essential to resolve the tightest pitches. Optimum CDSEM metrology conditions and EUV specific requirements such as full field correction with thru slit, flare and black border compensation are critical to improve the quality of the optical proximity correction (OPC) flows. OPC algorithms were used to maximize the process window by using width sizing and pitch shifting to meet lithographic printability criteria while pushing mask manufacturability constraints to their healthy limits. The sizing of metal lines is modelled and fed to the RC extraction flows to close the fabdesign house feedback loop to improve accuracy of timing closure. Multiple test masks were specifically designed to increase sensitivity of defect metrology and accelerate yield learning. Our results from multiple product vehicles demonstrate achievement of technology readiness milestones.

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“…Both the absorber thickness, which is large compared to the wavelength, and the oblique incidence contribute to a pronounced shadowing and other mask topography effects [5]. The mask shadowing effect is orientation-dependent relying on the mask topography, which induces a directional CD bias and shift on the order of several nanometers [5,6]. In contrast with a 22-nm or smaller half-pitch, the mask shadowing effect is too pronounced to be ignored and must be compensated.…”

Section: Introductionmentioning

confidence: 98%

“…In the past, a set of approaches were developed to resolve the imaging degradation attributed to flare [13][14][15], while some other methods were used to compensate the effect of shadowing or off-axis incidence [16][17][18][19]. Recently, comprehensive OPC methods incorporating OPE, flare, photoresist, and mask shadowing models in the optimization flow have been proposed so as to jointly consider these effects [5,[20][21][22][23][24][25]. However, most of these EUV OPC approaches are edge-based, which only move the segments of mask edges to optimize the imaging performance.…”

Section: Introductionmentioning

confidence: 99%

“…The EUV mask consists of Mo/Si multilayers and absorbers that reflect and absorb most of the EUV light energy, respectively. Both the absorber thickness, which is large compared to the wavelength, and the oblique incidence contribute to a pronounced shadowing and other mask topography effects [5]. The mask shadowing effect is orientation-dependent relying on the mask topography, which induces a directional CD bias and shift on the order of several nanometers [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Gradient-based inverse extreme ultraviolet lithography

Ma¹,

Wang²,

Chen³

et al. 2015

Appl. Opt.

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Extreme ultraviolet (EUV) lithography is the most promising successor of current deep ultraviolet (DUV) lithography. The very short wavelength, reflective optics, and nontelecentric structure of EUV lithography systems bring in different imaging phenomena into the lithographic image synthesis problem. This paper develops a gradient-based inverse algorithm for EUV lithography systems to effectively improve the image fidelity by comprehensively compensating the optical proximity effect, flare, photoresist, and mask shadowing effects. A block-based method is applied to iteratively optimize the main features and subresolution assist features (SRAFs) of mask patterns, while simultaneously preserving the mask manufacturability. The mask shadowing effect may be compensated by a retargeting method based on a calibrated shadowing model. Illustrative simulations at 22 and 16 nm technology nodes are presented to validate the effectiveness of the proposed methods.

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OPC modeling and correction solutions for EUV lithography

Cited by 11 publications

References 0 publications

Evaluation of CNN for fast EUV lithography simulation using iN3 logic mask patterns

Evaluation of CNN for fast EUV lithography simulation using iN3 logic mask patterns

Direct print EUV patterning of tight pitch metal layers for Intel 18A process technology node

Gradient-based inverse extreme ultraviolet lithography

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