In recent years, curvilinear mask technology has emerged as a next-generation resolution enhancement method for photomasks, providing optimal margins by maximizing the degree of freedom in pattern design. However, this technology presents challenges in defining the layout design rule limits based solely on geometric information, such as width, space, and corner-to-corner. With the introduction of multi-beam mask writers for curvilinear pattern production, a distinct set of defects that are difficult to pre-detect by conventional mask rule check have occurred, as these cannot be explained by geometry terms alone. In this study, we propose a deep learning-based mask check method, named mask deep check (MDC) for pre-detect defects in inspection. The proposed vector graphics transformer (VGT) uses the state-of-the-art transformer architecture, drawing an analogy between the vertices of curvilinear patterns and words in natural language. We demonstrate improved performance of VGT-based MDC compared to a traditional rule-based approach and a convolutional neural network-based MDC method. Importantly, VGT exhibits robustness in recall, ensuring that defective patterns are not misclassified as normal, thereby preventing missed defects. Moreover, by employing attention maps to visualize VGT results, we gain explainability and reveal that mask defects may arise from issues related to the fabrication of specific designs, rather than being solely attributable to geometric features. VGT-based MDC contributes to a better understanding of the challenges associated with curvilinear mask technology and offers an effective solution for detecting mask defects.
In recent years, Curvilinear Mask technology has emerged as a next-generation resolution enhancement method for photomasks, providing optimal margins by maximizing the degree of freedom in pattern design. However, this technology presents challenges in defining layout design rule limit based solely on geometric information rules based solely on geometric information such as width, space, and corner-to-corner. With the introduction of Multi Beam Mask Writers for Curvilinear pattern production, brand-new violations of Mask Rule Check(MRC) have occurred, which cannot be explained by geometry terms alone. In this study we propose a deep learning-based method for detecting MRC violations using the state-of-the-art Transformer architecture, drawing an analogy between the vertices of curvilinear patterns and words in natural language. The proposed MRC binary classifier demonstrates improved performance compared to traditional rule-based MRC and CNN-based MRC methods. Importantly, our method exhibits robustness in recall, ensuring that defective patterns are not misclassified as normal, preventing missed defects. Moreover, by employing attention maps to visualize deep learning results, we gain explainability and reveal that MRC violations may arise from issues related to the fabrication of specific designs, rather than being solely attributable to geometric features. This insight contributes to a better understanding of the challenges associated with Curvilinear Mask technology and offers an effective solution for detecting MRC violations.
As the integration node becomes smaller in 193nm ArF immersion optical lithography, the complexity of optical proximity correction (OPC) has been increased continuously. Moreover, pattern design should be changed by more aggressive transformation technique such as inverse lithography technique (ILT). The greater fidelity to the target design on wafers is achieved by the application of these OPC techniques and results in the greater complexity level of the mask patterns. Complicated mask pattern consists of many corners and assist features, which raises the fraction of small shots in e-beam data. To get more accurate mask pattern, the dose stability of small shots becomes more important in a complicated mask pattern. In this paper, we present the evaluation results of the small shot handling capabilities of e-beam machines. According to the results, the information of small shots generated during data fracturing should be considered as a factor that defines the complexity of patterns in e-beam writing. It shows that the small shot printing in e-beam machines need to be improved in order to guarantee mask pattern quality.
To overcome the resolution and throughput of current mask writer for advanced lithography technologies, the platform of e-beam writer have been evolved by the developments of hardware and software in writer. Especially, aggressive optical proximity correction (OPC) for unprecedented extension of optical lithography and the needs of low sensitivity resist for high resolution result in the limit of variable shaped beam writer which is widely used for mass production. The multi-beam mask writer is attractive candidate for photomask writing of sub-10nm device because of its high speed and the large degree of freedom which enable high dose and dose modulation for each pixel. However, the higher dose and almost unlimited appetite for dose modulation challenge the mask data processing (MDP) in aspects of extreme data volume and correction method. Here, we discuss the requirements of mask data processing for multi-beam mask writer and presents new challenges of the data format, data flow, and correction method for user and supplier MDP tool.
We design a C-shaped aperture which overcomes the diffraction limit of light to produce a high-brightness nano-size
light spot. For optical nano lithography, we construct a nano patterning system using an optical probe which adopts a
solid immersion lens (SIL), the 120 nm thickness aluminum film on the bottom surface of the SIL and the C-shaped
aperture engraved in the metal film. Light source is a diode laser of 405nm wavelength to expose h-line photoresist(PR).
A linear stage holding the optical probe makes the nano aperture contact with the PR coated on silicon wafer. Using this
patterning system, we obtain sub 100nm array patterns and measure the system performance in various exposure
conditions to verify the feasibility of plasmonic lithography.
In this paper, based on numerical study using the finite difference time domain method, we designed metal slits for
higher harmonic fringe patterns generated with surface plasmon interference lithography. The slits were designed to
generate higher fringe patterns having high intensity output, high contrast and good uniformity in sub-100nm scale.
After fabricating several types of slits on aluminum film mask according to the calculated designs with a focused ion
beam facility, lithography experiments using the aluminum slits were performed to record the near-filed fringe patterns
using i-line Hg lamp and SU-8 negative photoresist.
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