Resolution Enhancement Techniques Research Articles

Failure Characterization of Carbon Nanotube FETs Under Process Variations: Technology Scaling Issues

Carbon nanotube field-effect transistors (CNFETs), consisting of aligned semiconducting single-walled carbon nanotubes (CNTs), are promising nanoscale devices for implementation of high-performance, very dense, and low-power integrated circuits. Chemical synthesis and lithography processes are exploited to fabricate CNFETs. In the presence of subwavelength lithographic inaccuracies and chemical synthesis imperfections, the number of CNTs contained in a CNFET varies, which may lead to nonfunctional CNFETs. This paper presents a parameterized model for CNT-count distribution under combined lithographic and chemical synthesis imperfections. The proposed CNT-count model takes advantage of a new spatial CNT density metric which is easily and accurately extractable. We use the proposed model to investigate the impact of process variations on CNFET technology scaling. Results show that, to achieve 1-part-per-billion failure rate in 16-nm CNFET technology node, a synthesis process with a density of 450 CNTs/ $\mu\text{m}$ must be used when the CNFET width variation ratio is up to 20%. However, if the CNFET width variation ratio is reduced to 10% using a lithography resolution enhancement technique, a similar failure rate can be achieved with a chemical synthesis process that produces 150 CNTs/ $\mu\text{m}$ .

Quantitative Analysis of Metastasis Brain Tumor and its Area Estimation in MR Images

Metastasis brain tumor develops multiple tumors at asymmetrical location of the human brain. MRI Imaging is one of the prudent mechanisms to extract the tumor regions and to map the brain for diagnosing. For the better diagnosis, one must detect the tumor accurately and need to calculate the area and volume of the tumor exactly. Here in this letter, we proposed a novel resolution enhancement technique to improve the quality of MR brain image and optimized hybrid clustering with region split and merge algorithm to detect the tumor cells from the original MR images and to estimate the tumors from different locations. Simulation results show that the proposed algorithm has performed superior to conventional clustering algorithms such as Fuzzy C-means (FCM), KMeans and even optimized pillar algorithm.

Cardiac Light-Sheet Fluorescent Microscopy for Multi-Scale and Rapid Imaging of Architecture and Function.

Light Sheet Fluorescence Microscopy (LSFM) enables multi-dimensional and multi-scale imaging via illuminating specimens with a separate thin sheet of laser. It allows rapid plane illumination for reduced photo-damage and superior axial resolution and contrast. We hereby demonstrate cardiac LSFM (c-LSFM) imaging to assess the functional architecture of zebrafish embryos with a retrospective cardiac synchronization algorithm for four-dimensional reconstruction (3-D space + time). By combining our approach with tissue clearing techniques, we reveal the entire cardiac structures and hypertrabeculation of adult zebrafish hearts in response to doxorubicin treatment. By integrating the resolution enhancement technique with c-LSFM to increase the resolving power under a large field-of-view, we demonstrate the use of low power objective to resolve the entire architecture of large-scale neonatal mouse hearts, revealing the helical orientation of individual myocardial fibers. Therefore, our c-LSFM imaging approach provides multi-scale visualization of architecture and function to drive cardiovascular research with translational implication in congenital heart diseases.

An Efficient Method for Enhancing the Satellite Image Based on Multi Resolution Analysis

An image resolution enhancement technique which is based on the interpolation of the high frequency sub bands obtained by DWT is proposed in this paper. The proposed technique uses DWT to crumble an image into different sub bands, and then the high frequency sub band images have been interpolated. An original image is interpolated with half of the interpolation factor used for interpolation the high frequency sub bands. Afterwards all these images have been united using IDWT to produce a super determined image. The proposed technique has been experienced on well known benchmark images, where their PSNR, Mean Square Error shows the dominance of proposed technique over the predictable and state of art image resolution enhancement techniques.

Quantitative Analysis and Segmentation of Metastasis Brain Images using Hybrid Mean Shift Clustering

Metastasis brain tumor develops multiple tumors at asymmetrical location of the human brain. MRI imaging is one of the prudent mechanisms to extract the tumor regions and to map the brain for diagnosing. For the better diagnosis, one must detect the tumor accurately and need to calculate the area and volume of the tumor exactly. In this paper, we proposed a novel resolution enhancement technique to improve the quality of MR brain image and Optimized Hybrid Mean Shift Clustering (OHMSC) with region split and merge algorithm to detect the tumor cells from the original MR images and to estimate the tumors from different locations. Simulation results show that the proposed algorithm has performed superior to conventional clustering algorithms such as Fuzzy C-Means (FCM), K-Means and even optimized pillar algorithm.

Satellite Image Resolution Enhancement Using Non-Decimated Wavelet Transform and Gaussian Mixture Model

High Resolution Satellite Images are of a significant importance in many fields of research. For the last few decades Wavelets are playing a key role in image resolution enhancement techniques. In those algorithms, Discrete Wavelet Transform (DWT) is mostly used in image decomposition stage and bicubic interpolation is used in interpolation stage. In this paper, we proposed a new technique based on the image decomposition using DWT and the interpolation using Gaussian Mixture Model (GMM) which is a parametric probability density function represented as a weighted sum of Gaussian component densities instead of weighted sum of neighborhood pixels such as bicubic or bilinear interpolations. This DWT image decomposition and GMM interpolation gives better results than existing techniques and it is proved with the quantitative (peak signal-to-noise ratio and quality index) and visual results over the conventional and state-of-art image resolution enhancement techniques.

Resolution Enhancement of the SatelliteImage Processing using DT CWT Techniques

An image is defined as an array, or a matrix, of square pixels (elements of picture) arranged in rows and columns. Image processing is a procedure of converting an image into digital form and carry out some operation on it, in order to get an improved image and take out several helpful information from it. Actually Signal or image processing enhances certain features of the data while suppressing others. For instance, in analysing a fingerprint image against a textured background it may be important to enhance the fingerprint to identify its owner. The appropriate processing would need to focus on features such as the overall texture pattern to be suppressed and the fingerprint's parallel, smoothly curving lines to be enhanced. Typical Image & Signal Processing Applications are used for Computer vision, Face detection ,Feature detection, extraction, and analysis ,Medical image processing, Remote sensing, Speech recognition, Speech synthesis, Speech compression, Audio, noise suppression, Automated map analysis. In this paper Resolution enhancement (RE) schemes (which are not based on wavelets) suffer from the drawback of losing highfrequency contents (which results in blurring). The discrete- wavelet-transform-based (DWT) RE scheme generates artifacts (due to a DWT shift-variant property). A wavelet-domain approach based on dual-tree complex wavelet transform (DT-CWT) and nonlocal means (NLM) is proposed for RE of the satellite images. A satellite input image is decomposed by DT-CWT (which is nearly shift invariant) to obtain high-frequency subbands. The high-frequency subbands and the low-resolution (LR) input image are interpolated using the Lanczos interpolator. The high- frequency subbands are passed through an NLM filter to cater for the artifacts generated by DT-CWT (despite of its nearly shift invariance). The filtered high-frequency subbands and the LR input image are combined using inverse DT-CWT to obtain a resolution-enhanced image. Objective and subjective analyses reveal superiority of the proposed technique over the conventional and state-of-the-art RE techniques.

Quantum limits to optical point-source localization

Motivated by the importance of optical microscopes to science and engineering, scientists have pondered for centuries how to improve their resolution and the existence of fundamental resolution limits. In recent years, a new class of microscopes that overcome a long-held belief about the resolution have revolutionized biological imaging. Termed "superresolution" microscopy, these techniques work by accurately locating optical point sources from far field. To investigate the fundamental localization limits, here I derive quantum lower bounds on the error of locating point sources in free space, taking full account of the quantum, nonparaxial, and vectoral nature of photons. These bounds are valid for any measurement technique, as long as it obeys quantum mechanics, and serve as general no-go theorems for the resolution of microscopes. To arrive at analytic results, I focus mainly on the cases of one and two classical monochromatic sources with an initial vacuum optical state. For one source, a lower bound on the root-mean-square position estimation error is on the order of $\lambda_0/\sqrt{N}$, where $\lambda_0$ is the free-space wavelength and $N$ is the average number of radiated photons. For two sources, owing to the statistical effect of nuisance parameters, the error bound diverges when their radiated fields overlap significantly. The use of squeezed light to enhance further the accuracy of locating one classical point source and the localization limits for partially coherent sources and single-photon sources are also discussed. The presented theory establishes a rigorous quantum statiscal inference framework for the study of superresolution microscopy and points to the possibility of using quantum techniques for true resolution enhancement.

Robust hybrid source and mask optimization to lithography source blur and flare.

As a promising resolution enhancement technique, a set of pixelated source and mask optimization (SMO) methods has been introduced to further improve the lithography at 45 nm node and beyond. Recently, some papers studied the impact of the scanner errors on SMO, and the results revealed that the source blur and flare seriously impact on the lithography performance of the optimal source and mask resulting from SMO. However, current SMO methods did not propose an effective method to compensate for the impact of these nonideal factors of the actual scanners. To overcome this drawback, this paper focuses on developing a robust hybrid SMO (HSMO) method where the sensitivities of the aerial image to source blur and flare are introduced into the cost function. Simulation results are compared with traditional SMO to demonstrate the benefit of the proposed source blur-flare-aware HSMO method in pattern fidelity and process window.

Gradient-based joint source polarization mask optimization for optical lithography

Source and mask optimization (SMO) has emerged as a key resolution enhancement technique for advanced optical lithography. Current SMO, however, keeps the polarization state fixed, thus limiting the degrees of freedom during the optimization procedure. To overcome this limitation, pixelated gradient-based joint source polarization mask optimization (SPMO) approaches, which effectively extend the solution space of the SMO problem by introducing polarization variables, are developed. First, the SPMO framework is formulated using an integrative and analytic vector imaging model that is capable of explicitly incorporating the polarization angles. Subsequently, two optimization methods, namely simultaneous SPMO (SISPMO) and sequential SPMO (SESPMO) are developed, both of which exploit gradient-based algorithms to solve for the optimization problem. In addition, a postprocessing method is applied to reduce the complexity of the optimized polarization angle pattern for improving its manufacturability. Illustrative simulations are presented to validate the effectiveness of the proposed algorithms. The simulations also demonstrate the superiority of the SESPMO over SISPMO in computational efficiency and improvement of image fidelity.

Fast Level-Set-Based Inverse Lithography Algorithm for Process Robustness Improvement and Its Application

Inverse lithography technology (ILT) is one of the promising resolution enhancement techniques (RETs), as the advanced integrated circuits (IC) technology nodes still use the 193 nm light source. Among all the algorithms for ILT, the level-set-based ILT (LSB-ILT) is a feasible choice with good production result in practice. However, existing ILT algorithms optimize masks at nominal process condition without giving sufficient attention to the process variations, and thus the optimized masks show poor performance with focus and dose variations. In this paper, we put forward a new LSB-ILT algorithm for process robustness improvement with fast convergence. In order to account for the process variations in the optimization, we adopt a new form of the cost function by adding the objective function of process variation band (PV band) to the nominal cost. We also adopt the hybrid conjugate gradient (CG) method to reduce the runtime of the algorithm. We perform experiments on ICCAD 2013 benchmarks and the results show that our algorithm outperforms the top two winners of the ICCAD 2013 contest by 6.5%. We also adopt the attenuated phase shift mask (att-PSM) in the experiment with test cases from industry. The results show that our new algorithm has a fast convergence speed and reduces the process manufacturability index (PMI) by 38.77% compared with the LSB-ILT algorithm without the consideration of PV band.

GPU accelerated parallel FFT processing for Fourier transform hyperspectral imaging

Sequentially processing massive 1D fast Fourier transformations (FFT) on raw interferograms using a CPU has limited the speed of conventional Fourier transform imaging spectrometers (FTIS). This paper reports the implementation of highly paralleled FFT computation using a low-end graphics processing unit (GPU) device for acceleration of this process. Comparison experiment results have demonstrated ∼10–30 times acceleration improvement using GPU-based parallel processing over conventional CPU-based serial processing upon the input data with same sizes: GPU processing time of only 630 and 173 ms of datacubes with 512×512×1024 and 64×64×16 k pixels, respectively, has presented its potential for online and even real-time FTIS raw data processing. The addition of a cheap GPU device into any FTIS system involves no optical modifications, so it is a highly cost-effective technique for temporal resolution enhancement of FTIS-based hyperspectral imaging applications.

Simultaneous Reconstruction and Resolution Enhancement for Magnetic Particle Imaging

Spatial resolution is an essential parameter for magnetic particle imaging (MPI). The spatial resolution of MPI depends, among other things, on the particles diameter and sampling frequency. The spatial resolution increases when increasing the particle diameter. However, large particles suffer from relaxation effects and are not preferred in some applications. On the other hand, spatial resolution increases with the sampling frequency, which in turn increases the number of sampling points. As an alternative solution for resolution enhancement, super-resolution (SR) is proved to be beneficial in improving the image quality of many medical imaging systems without the need for significant hardware alteration. In this paper, we propose to use small particle diameter and low sampling frequency to obtain multiple low-resolution (LR) magnetic particle measurements and apply a resolution enhancement technique to reconstruct a higher resolution magnetic particle concentration. Unlike the conventional SR techniques, we propose to reconstruct a high-resolution concentration from the measured LR signals instead of reconstructing LR concentrations and then post-process these concentrations to get a higher resolution concentration. Simulation results show that the resolution of MPI can be increased by incorporating resolution enhancement technique without increasing the particle diameter. In addition, simulation results show that using simultaneous reconstruction and resolution enhancement results in sharper concentrations and that this procedure is more robust against noise than using SR as a post-process.

Satellite Image Resolution Enhancement Using Dyadic-integer Coefficients Based Bi-orthogonal Wavelet Filters

Abstract The acquisition of high satellite Imagery becomes difficult due to the instrument limitation and imperfect imaging optics. Low spatial resolution satellite images consist a lot of mixed pixels which degrade the detection and recognition performance in civil and military applications. In this paper, a satellite image resolution enhancement using Dyadic-Integer coefficients based bi-orthogonal wavelet filters is proposed. Dyadic-integer-coefficient based wavelet filters are derived from the construction of a half-band polynomial. The splitting approach is used to develop the integer-coefficient based half-band polynomial. The possibility of these dyadic-integer coefficients based wavelet filters is explored in the field of image enhancement using sub-pixel image registration. The two-resolution frames are registered at a specific shift from one another to restore the resolution lost by the sensors. The discrete wavelet transform (DWT) obtained from the designed coefficients is applied on these two low-resolution images to obtain the high resolution image. The proposed method has been tested on satellite images. The quantitative (peak signal-to-noise ratio and root mean square error) and visual results show the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques.

Efficient three-dimensional resist profile-driven source mask optimization optical proximity correction based on Abbe-principal component analysis and Sylvester equation

As one of the critical stages of a very large scale integration fabrication process, postexposure bake (PEB) plays a crucial role in determining the final three-dimensional (3-D) profiles and lessening the standing wave effects. However, the full 3-D chemically amplified resist simulation is not widely adopted during the postlayout optimization due to the long run-time and huge memory usage. An efficient simulation method is proposed to simulate the PEB while considering standing wave effects and resolution enhancement techniques, such as source mask optimization and subresolution assist features based on the Sylvester equation and Abbe-principal component analysis method. Simulation results show that our algorithm is 20× faster than the conventional Gaussian convolution method.

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

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.

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

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.

Optical proximity correction using holographic imaging technique

In this work, the preliminary results of the holographic imaging technique (HIT) as a resolution enhancement technique (RET) hybrid inverse lithography technique (ILT) optical proximity correction mask correction is presented. The proposed technique is based on the illumination system and free space propagation of the mask. First, an in-line hologram (Gabor hologram) is generated by computing the target pattern diffraction pattern. Second, the computed hologram is synthesized as pattern contours to form a mask for a conventional optical system. In this study, a simple HIT corrected mask is validated with compact lithographic exposure system mathematical model for demonstrating the preliminary validity of the proposed method. Future plans to validate HIT for the most aggressive process nodes are proposed. The proposed technique has not been exercised against conventional ILT or other RET techniques, and does not contain analysis of resolution improvement achievable with those techniques.

A DWT-SWT based Image Super Resolution with Multi Surface Fitting

paper, an image super-resolution technique is proposed which is based on interpolation of high frequency sub-band images obtained by Discrete Wavelet Transform on input image. In those sub-bands edges are enhanced by introducing an intermediate stage by using Stationary Wavelet Transform. The wavelet transform is applied in order to decompose image into different sub-bands. In those sub-bands, the high frequency sub-bands are interpolated and then these estimated high frequency sub-bands are modified by using high frequency sub-bands obtained through SWT. These all sub- bands are fused to generate a new high resolution by using inverse Wavelet transform techniques. The proposed results depict the conventional and state of art image resolution enhancement techniques.

Hybrid approach for the design of mirror array to produce freeform illumination sources in immersion lithography

Source and mask optimization (SMO) has emerged as a key resolution enhancement technique (RET) for 45nm technology node and below in lithography. The design method of freeform illumination sources predicted by SMO is significant for the scanner development. We present a hybrid approach combining simultaneous and sequential approaches to optimize the tilt angles of the mirrors to produce multi target freeform illumination sources accurately and quickly. The size of spot reflect by plane mirror can be easily controlled by changing different microlens arrays with appropriate focal length, which reduce the complexity of the system and makes it more flexible to produce the specific freeform sources compared to curving the mirrors used in previous work. The relationship between the tilt angles of plane mirrors and the positions of the spots in the pupil is obtained by chief ray tracing. Using the hybrid approach the freeform illumination sources required by SMO can be designed by merely adjusting the tilt angles of mirrors without changing other parameters of optical elements, which is most effective for both lithography tool manufacture and its applications. The real ray tracing results demonstrate that our design method is capable of creating multi freeform illumination sources with high transmittance, and confirm that the effectiveness of the hybrid approach for optimized design and control of mirror array in immersion lithography system.