Aerial image back propagation with two-dimensional transmission cross coefficient

Yamazoe, Kenji; Sekine, Yoshiyuki; Honda, Tokuyuki

doi:10.1117/1.3206980

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…A simplification introduced by Cobb et al [ 16 ] and later further developed by Yamazoe et al [ 17 , 18 ] reduces this 4D matrix into a set of 2D-filter kernels by applying the singular-value decomposition (SVD). The result will be a set of eigenvectors which acts as filter kernels and its eigenvalues which gives the weights.…”

Section: Theorymentioning

confidence: 99%

Using machine-learning to optimize phase contrast in a low-cost cellphone microscope

et al. 2018

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Cellphones equipped with high-quality cameras and powerful CPUs as well as GPUs are widespread. This opens new prospects to use such existing computational and imaging resources to perform medical diagnosis in developing countries at a very low cost. Many relevant samples, like biological cells or waterborn parasites, are almost fully transparent. As they do not exhibit absorption, but alter the light’s phase only, they are almost invisible in brightfield microscopy. Expensive equipment and procedures for microscopic contrasting or sample staining often are not available. Dedicated illumination approaches, tailored to the sample under investigation help to boost the contrast. This is achieved by a programmable illumination source, which also allows to measure the phase gradient using the differential phase contrast (DPC) [1, 2] or even the quantitative phase using the derived qDPC approach [3]. By applying machine-learning techniques, such as a convolutional neural network (CNN), it is possible to learn a relationship between samples to be examined and its optimal light source shapes, in order to increase e.g. phase contrast, from a given dataset to enable real-time applications. For the experimental setup, we developed a 3D-printed smartphone microscope for less than 100 $ using off-the-shelf components only such as a low-cost video projector. The fully automated system assures true Koehler illumination with an LCD as the condenser aperture and a reversed smartphone lens as the microscope objective. We show that the effect of a varied light source shape, using the pre-trained CNN, does not only improve the phase contrast, but also the impression of an improvement in optical resolution without adding any special optics, as demonstrated by measurements.

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Section: Theorymentioning

confidence: 99%

Using machine-learning to optimize phase contrast in a low-cost cellphone microscope

et al. 2018

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“…The results revealed an increase in DOF of the isolated contact holes by more than 400 nm. Kawashima et al and Hakkoet alused the two-dimensional transmission cross coefficient (2D-TCC) technique as the basis for an algorithm [17], [18] and discovered that the simplified optical model could quickly determine potential SRAF sites and apply these in OPC. However, the technique was unable to further enhance the quality of aerial images, and the gradient descent algorithm used further complicated the computation by requiring the calculation of the gradient between an optimized target pattern and the corrected photomask [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Placement of Sub-Resolution Assist Features Based on a Genetic Algorithm

Kan

Huang

et al. 2019

IEEE Access

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Resolution enhancement techniques compatible with an ArF (193 nm) immersion optical lithography system may constitute an effective means of minimizing the size of technology nodes of the dynamic random access memory. This paper investigated one such technique, namely mask optimization (MO), and applied sub-resolution assist features (SRAFs) in the MO to improve the aerial image quality of a target pattern that had undergone optical proximity correction (OPC). This paper first developed an optical model based on the Hopkins model to create an interference map, which was then used to create a cutlevel map. The cut-level map was instrumental in predicting potential SRAF sites and randomly generating SRAFs that would serve as the initial population for a genetic algorithm (GA). Chromosomes were defined as a section map and encoded genes were used to define SRAF and target pattern. Using a GA to identify SRAF geometric measurements and placement was revealed to increase the process window and improve the image performance of the target pattern. This paper used 1D and 2D line/space (L/S) images as the baseline to test the convergence of the proposed method. 2D images were also used to test improvements in aerial image performance. The results indicated that the 1D L/S pattern converged at the 100th iteration. Furthermore, the depths of the focus of the 2D L/S array and 2D contact hole patterns were successfully increased by 113 and 21 nm, respectively. The proposed SRAF method, which integrated the GA and interference map, was able to ensure the diversity of potential SRAF solutions. Moreover, it was able to restrict the SRAF solutions to rectangular structures through the application of mask rules, thereby reducing the cost and improving the feasibility of photomasks. INDEX TERMS Sub-resolution assist feature (SRAF), interference map, genetic algorithm (GA), mask optimization, depth of focus (DOF).

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Aerial image back propagation with two-dimensional transmission cross coefficient

Cited by 2 publications

References 17 publications

Using machine-learning to optimize phase contrast in a low-cost cellphone microscope

Using machine-learning to optimize phase contrast in a low-cost cellphone microscope

Placement of Sub-Resolution Assist Features Based on a Genetic Algorithm

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