Resolution Enhancement Techniques Research Articles

A review of state-of-the-art resolution improvement techniques in SPECT imaging.

Single photon emission computed tomography (SPECT), a technique capable of capturing functional and molecular information, has been widely adopted in theranostics applications across various fields, including cardiology, neurology, and oncology. The spatial resolution of SPECT imaging is relatively poor, which poses a significant limitation, especially the visualization of small lesions. The main factors affecting the limited spatial resolution of SPECT include projection sampling techniques, hardware and software. Both hardware and software innovations have contributed substantially to improved SPECT imaging quality. The present review provides an overview of state-of-the-art methods for improving spatial resolution in clinical and pre-clinical SPECT systems. It delves into advancements in detector design and modifications, projection sampling techniques, traditional reconstruction algorithm development and optimization, and the emerging role of deep learning. Hardware enhancements can result in SPECT systems that are both lighter and more compact, while also improving spatial resolution. Software innovations can mitigate the costs of hardware modifications. This survey offers a thorough overview of the rapid advancements in resolution enhancement techniques within the field of SPECT, with the objective of identifying the most recent trends. This is anticipated to facilitate further optimization and improvement of clinical systems, enabling the visualization of small lesions in the early stages of tumor detection, thereby enhancing accurate localization and facilitating both diagnostic imaging and radionuclide therapy, ultimately benefiting both clinicians and patients.

Enhancing Mammography Images with Artificial Intelligence to Improve Radiological Diagnosis in Breast Cancer

Breast cancer is one of the most common types of cancer in women, and early diagnosis is life-saving. The aim of this study is to enhance the resolution of mammography images, thereby improving the contrast resolution, spatial resolution, and the detectability of calcifications, distortions, and opacities in the images. For this purpose, mammography images obtained from the open-access mini-MIAS dataset were used. Both the original dataset and the images processed with the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm underwent resolution enhancement using the Stable Diffusion artificial intelligence system. The results were evaluated by an expert radiologist, and it was determined that the diagnostic quality of the images significantly increased. These improvements aim to support early diagnosis in breast cancer and enhance diagnostic accuracy. Additionally, the applicability and effectiveness of these methods were emphasized, and the potential benefits of resolution enhancement techniques in clinical practice were discussed. The results have the potential to allow for more detailed and accurate analysis of mammography images, thereby improving patient care and treatment planning.

High efficiency regulation method to generate an illumination source by adopting the micromirror array in DUV lithography

Source and mask optimization (SMO) is an important lithography resolution enhancement technique for 22 nm technology nodes and beyond in lithography. This technique generates freeform sources by adjusting the inclination angle of the micromirror array (MMA). In this paper, a high efficiency regulation method to generate an illumination source by adopting the micromirror array in DUV lithography is proposed. The number and inclination angles of micromirrors required for adjustment to convert the initial source into any illumination source are quickly and accurately obtained by using the proposed method. The illumination source is obtained on the pupil plane by adjusting the inclination angles of some micromirrors. The simulation results show that the initial source is converted into another illumination source with different complexities by adjusting some micromirrors. Compared with the traditional MMA allocation method, when the initial source is the freeform source, the regulation efficiency of micromirrors has been improved by at least 20%. The method reduces the regulation time of micromirrors and the number of micromirrors required for adjustment. Moreover, the number of micromirrors required for adjustment is positively correlated with the coherence coefficient. The results illustrate that this method is effective and has significant engineering implications.

Angle monitor of micromirror array for freeform illumination in lithography systems

Source and mask optimization is a critical technique for further resolution enhancement in immersion lithography systems, wherein the optimal illumination source shape is widely generated by the micromirror array. Accordingly, the accurate achievement of the allocated angles of micromirrors is a key prerequisite for the implementation of arbitrary illumination patterns. In this paper, we propose an angle monitor to ensure the high-precision tilting of thousands of biaxial micromirrors. As one of the critical modules for closed-loop control of the two-dimensional micromirror array, the online monitor feeds the monitored high-precision tilt angle back to the processing system. The angle monitor mainly consists of the spot scanning module and the angle detection module. Among them, an f-θ lens and a Fourier transform lens with satisfied performances are designed and evaluated by CODE V. Furthermore, a galvanometer and the designed f-θ lens are adopted for the generation of the spot array irradiated on the electrostatic actuated mirrors. Meanwhile, the designed Fourier transform lenses are employed to detect the corresponding tilt angles. In addition, the performance of the proposed system is identified by simulations and experiments individually. It is demonstrated that the monitorable biaxial tilt range of the system is (−2.5°, +2.5°) with a repeatability of better than 0.005°. The large-format micromirror array can be monitored completely without optical cross-talk. Through the device, a 16 × 16 micromirror array is monitored, where the initial angle with no bias voltage applied is captured and the voltage–angle relation for individual micromirrors is obtained. In general, the proposed system can be utilized in the illumination system, providing an efficient and reliable method for complex source shape generation.

CTM-SRAF: Continuous Transmission Mask-Based Constraint-Aware Subresolution Assist Feature Generation

In lithography process, Sub-Resolution Assist Features (SRAFs), as an essential resolution enhancement technique (RET), is applied to improve the pattern fidelity and enlarge the process window. In this paper, we propose a robust constraint-aware SRAF generation method based on continuous transmission mask (CTM). The intensity distribution on the continuous transmission mask is extracted to guide the SRAF generation. The SRAF insertion also honors the design rules, which is formulated as integer programming with quadratic constraints and solved by a fast yet efficient algorithm. A fast probe-based SRAF evolution method is proposed to determine the shapes of SRAFs. The effectiveness and efficiency are demonstrated based on the experimental results.

Design and large-scale nanofabrication of plasmonic solar light absorbers

Surface plasmon resonances have been exploited for many applications due to their tunability, which is directly related to the geometry of nanostructures. Based on their dimension and material stack, the resonances can be tailored to achieve high absorbing or reflecting nanopatterned surfaces designed for specific wavelengths. While the preferred lithographic printing techniques in the field allow high precision and control of the structures, they are limited in throughput, thus restricting possible large-scale applications. In this work, we present a full process flow, which can produce hundreds of square meters of nanopillar arrays by combining resolution enhancement techniques (RETs) on a deep-UV stepper for fabricating a silicon master and roll-to-roll extrusion coating (R2R-EC) for its replication. We demonstrate optimized exposures with the combination of dipole off-axis illumination, triple cross-exposure, and the addition of assisted features on the mask design. By simulating the RETs compared to a conventional setup, we show how lithographic parameters such as the normalized image log-slope (NILS) improve from 0.90 to 2.05 or the resist image contrast (RIC) increases from 0.429 to 0.813. We confirm these results by printing wafer-size hexagonal and rectangular arrays of nanopillars with 340, 350, and 360 nm pitches and diameters ranging from 100 to 200 nm. We show the successful replication of both designs by R2R-EC, an industrial process, which produces hundred-meter rolls of patterned polymer. We demonstrate that after metallization, the samples are suitable for solar absorption by measuring their absorptance (absorbed to incident intensity) and comparing it with the solar irradiance peak. We achieve a 70% efficiency for both hexagonal and rectangular arrays at resonant peaks of 550 and 600 nm, respectively, where the hexagonal array better matches the solar irradiance peak. Additionally, the plasmonic samples block 78% of the heat radiation when compared to a plain black polymer foil for reference, making them more efficient for solar harvesting applications.

Application of resolution enhancement techniques on medical images

Application of resolution enhancement techniques on medical images

MAS-Net OCT: a deep-learning-based speckle-free multiple aperture synthetic optical coherence tomography.

High-resolution spectral domain optical coherence tomography (SD-OCT) is a vital clinical technique that suffers from the inherent compromise between transverse resolution and depth of focus (DOF). Meanwhile, speckle noise worsens OCT imaging resolving power and restricts potential resolution-enhancement techniques. Multiple aperture synthetic (MAS) OCT transmits light signals and records sample echoes along a synthetic aperture to extend DOF, acquired by time-encoding or optical path length encoding. In this work, a deep-learning-based multiple aperture synthetic OCT termed MAS-Net OCT, which integrated a speckle-free model based on self-supervised learning, was proposed. MAS-Net was trained on datasets generated by the MAS OCT system. Here we performed experiments on homemade microparticle samples and various biological tissues. Results demonstrated that the proposed MAS-Net OCT could effectively improve the transverse resolution in a large imaging depth as well as reduced most speckle noise.

Intelligent diagnosis of flip chip solder joints with resolution enhanced SAM image

Scanning acoustic microscopy (SAM) technique has been applied to defect inspection in electronic devices. With the increase of packaging density, detection of the micro-defects in high density devices becomes more and more challenging. The SAM test is suffering from sacrificing the spatial resolution to reach a certain penetration depth of the ultrasonic waves. So it is necessary to enhance the resolution level of the SAM image. In this paper, a wavelet based resolution enhancement technique was investigated to reconstruct a high quality image for SAM test of the flip chip packages. The stationary wavelet transform was adopted to decompose the captured SAM image into four frequency subbands, and the high frequency subbands were enhanced by adding the difference matrix in the intermediate stage, and a super resolution SAM image was derived from combining all the subbands by using the Inverse Discrete Wavelet Transform. Then the solder joints segmented from the SR-SAM image were classified by using the SVM algorithm. The results validated that the proposed technique is effective to improve the detection accuracy of SAM test.

A GPU-Enabled Level-Set Method for Mask Optimization

As the feature size of advanced integrated circuits keeps shrinking, resolution enhancement techniques (RETs) are utilized to improve the printability in the lithography process. Optical proximity correction (OPC) is one of the most widely used RETs aiming at compensating the mask to generate a more precise wafer image. In this article, we put forward a level-set-based OPC approach with high mask optimization quality and fast convergence. In order to suppress the disturbance of the condition fluctuation in the lithography process, we propose a new process window-aware cost function. Then, a novel momentum-based evolution technique is adopted, which demonstrates substantial improvement. We also propose a self-adaptive conjugate gradient method that promises a higher optimization stability and less consuming time. Moreover, the graphics processing unit (GPU) is leveraged for accelerating the proposed algorithm. We take the output masks from a machine learning-based mask optimization flow as the input and work as the postprocess to refine the quasi-optimized masks. Experimental results on ICCAD 2013 benchmarks show that our algorithm outperforms all previous OPC algorithms in both solution quality and runtime overhead.

Low light image enhancement using curvelet transform and iterative back projection

With the advancement of technology in image capturing, people are accustomed to high-resolution images. One of the primary necessities of an image capturing system is to provide the same. However, in many cases, the image resolution may not be reaching the expectations of the user which leads to a decrease in user experience. This is a common phenomenon that occurs when the images are captured in low light or if the image encounters a distortion either because of lack of exposure or the image capturing devices may be equipped with a small size sensor. In this work, a resolution enhancement technique using the concepts of curvelet transform and iterative back projection is presented. Sparse representation of images can be enhanced using a combination of curvelet transforms with iterative back projection. Application of curvelet transform along with iterative back projection algorithm on low light images results in enhancing the resolution of the images. The resultant images from here then passed through the inverse transform block and gives an image with contrast enhancement which leads to the user experience improvement. The antiquated image enhancement with improvement in the resolution is validated with the measurement of peak signal-to-noise ratio and structural similarity index. The usage of curvelet transform with iterative back projection leads to the restoration of the image resolution by minimizing the distortions, thus leading to an enhanced image whose edge details are retained.

Machine-Learning-Based Inversion Scheme for Super-Resolution Three-Dimensional Microwave Human Brain Imaging

To realize efficient three-dimensional (3-D) super-resolution whole brain microwave imaging, a new machine- learning-based inversion method with a resolution enhancement technique is proposed. It consists of three parts: a parallel semiconnected backpropagation neural network (SJ-BPNN) scheme, a U-Net scheme, and a modified Akima piecewise cubic Hermite interpolation (MAPCHI) scheme. The parallel SJ-BPNN scheme is first employed to map the measured scattered field data to the preliminary electrical properties’ distribution of human brain. Then, U-Net is used to improve the quality of these preliminary reconstruction results. Finally, the MAPCHI scheme is adopted to greatly improve the resolution of reconstruction results with a very low computational cost. Numerical examples of a normal human brain and a human brain with abnormal scatterers show that the proposed method can achieve accurate high-resolution human brain imaging with 1024 × 1024 × 1024 voxels with a very low computational cost.

Fractal Feature Based Image Resolution Enhancement Using Wavelet–Fractal Transformation in Gradient Domain

The fractal geometries are applied extensively in many applications like pattern recognition, texture analysis and segmentation. The application of fractal geometry requires estimation of the fractal features. The fractal dimension and fractal length are found effective to analyze and measure image features, such as texture, resolution, etc. This paper proposes a new wavelet–fractal technique for image resolution enhancement. The resolution of the wavelet sub-bands are improved using scaling operator and then it is transformed into texture vector. The proposed method then computes fractal dimension and fractal length in gradient domain which is used for resolution enhancement. It is observed that by using scaling operator in the gradient domain, the fractal dimension and fractal length becomes scale invariant. The major advantage of the proposed wavelet–fractal technique is that the feature vector retains fractal dimension and fractal length both. Thus, the resolution enhanced image restores the texture information well. The texture information has also been observed in terms of fractal dimension with varied sample size. We present qualitative and quantitative analysis of the proposed method with existing state of art methods.

Deconvolution-Based Partial Volume Correction for Volumetric Blood Flow Measurement.

Ultrasound-based blood flow (BF) monitoring is vital in the diagnosis and treatment of a variety of cardiovascular and neurologic conditions. Finite spatial resolution of clinical color flow (CF) systems, however, has hampered measurement of vessel cross Section areas. We propose a resolution enhancement technique that allows reliable determination of BF in small vessels. We leverage sparsity in the spatial distribution of the frequency spectrum of routinely collected CF data to blindly determine the point spread function (PSF) of the imaging system in a robust manner. The CF data are then deconvolved with the PSF, and the volumetric flow is computed using the resulting velocity profiles. Data were collected from phantom blood vessels with diameters between 2 and 6 mm using a clinical ultrasound system at 2 MHz insonation frequency. The proposed method yielded a flow estimation bias of 0 mL/min, standard deviation of error (SDE) of 22 mL/min, and a root-mean-square error (RMSE) of 22 mL/min over a 150 mL/min range of mean flows. Recordings were also obtained in low signal-to-noise ratio (SNR) conditions using a skull mimicking element, resulting in an estimation bias of -13 mL/min, SDE of 23 mL/min, and an RMSE of 26 mL/min. The effect of insonation frequency was also investigated by obtaining recordings at 4.3 MHz, yielding an estimation bias of -16 mL/min, SDE of 16 mL/min, and an RMSE of 22 mL/min. The results indicate that our technique can lead to clinically acceptable flow measurements across a range of vessel diameters in high and low SNR regimes.

Improved generalized S-transform deconvolution for non-stationary seismic data

Improving the vertical resolution is one of the significant tasks for seismic data processing. Most traditional resolution-enhancement techniques assume that the seismic wavelet is time-invariant. However, the seismic wavelet varies with seismic wave propagation in the subsurface. To solve this issue, a new spectral-modeling method is proposed to extract the time-varying wavelet using improved generalized S-transform (IGST) and higher-order Fourier series. The IGST based on modified time-window function can effectively improve the resolution of the time-frequency (t-f) spectrum. The high-order Fourier series is used to fit on the logarithm t-f spectrum and achieve the high-precision time-varying wavelet. The proposed method is composed of four steps in the implementation. Firstly, the seismic data is decomposed by the IGST and converted to the logarithm t-f domain. Secondly, the time-varying wavelet spectrum is modeled at each time sample using a higher-order Fourier series. Thirdly, the boxcar smoothing method is used to smooth the time-varying wavelet spectrum and extract the time-varying wavelet with Hilbert transform. Finally, using the time-varying wavelet spectrum to spectrally balance seismic data to flatten the seismic response. Synthetic and field data examples demonstrate the feasibility of the proposed method in improving the signal-to-noise ratio and enhancing the vertical resolution.

Adaptive optical microscopy via virtual-imaging-assisted wavefront sensing for high-resolution tissue imaging

Adaptive optics (AO) is a powerful tool for optical microscopy to counteract the effects of optical aberrations and improve the imaging performance in biological tissues. The diversity of sample characteristics entails the use of different AO schemes to measure the underlying aberrations. Here, we present an indirect wavefront sensing method leveraging a virtual imaging scheme and a structural-similarity-based shift measurement algorithm to enable aberration measurement using intrinsic structures even with temporally varying signals. We achieved high-resolution two-photon imaging in a variety of biological samples, including fixed biological tissues and living animals, after aberration correction. We present AO-incorporated subtractive imaging to show that our method can be readily integrated with resolution enhancement techniques to obtain higher resolution in biological tissues. The robustness of our method to signal variation is demonstrated by both simulations and aberration measurement on neurons exhibiting spontaneous activity in a living larval zebrafish.

Resolution technology of lithography machine

Photolithography is one of the core methods in the semiconductor industry for the mass production of integrated circuits (IC). It is also the driving force behind Moore’s Law, which predicts the number of transistors in an integrated circuit to double every two years. This paper aims to overview the photolithography process and its current situations, starting with the rationale behind it and its advantages. We review the photolithography process in individual steps and gave typical process parameters when applicable. Then we introduce the major photolithography system manufacturers of interest, followed by an overview of techniques used to improve the resolution of photolithographic systems, namely immersion lithography, Extreme-Ultraviolet (EUV) lithography, and Resolution Enhancement Techniques (RETs). Finally we discuss the challenges encountered in lithography technology.

Correction to: Range resolution enhancement technique for dual demodulator continuous-time frequency modulation processing

Correction to: Range resolution enhancement technique for dual demodulator continuous-time frequency modulation processing

Improved Resolution Enhancement Technique for Broadband Illumination in Flat Panel Display Lithography

Improved Resolution Enhancement Technique for Broadband Illumination in Flat Panel Display Lithography

Sub-resolution assist feature placement with generative adversarial network

Background: As the design layout of integrated circuits (ICs) is continually scaling down, sub-resolution assist features (SRAF) have been extensively used in resolution enhancement technique applications to enhance lithography printing fidelity and widen the manufacturing process window (PW). With conventional SRAF insertion techniques, rule-based SRAF (RB-SRAF) and model-based SRAF (MB-SRAF) methods have since been widely adopted. Aim: The typical RB-SRAF is an efficient method to generate SRAFs consistently for simple designs but cannot be optimized for multiple critical patterns or complex layout schemes. Although MB-SRAF is able to achieve better PW as well as reducing conflicts between placement rules and clean-up rules, many iterations for convergence and extremely high computational costs are required. The explosion of machine learning techniques could facilitate the complex processes of mask optimization, such as SRAF insertion. Approach: Generative adversarial network was studied on a Via layer of advanced 3D NAND flash memory, by training target images and Inverse Lithography Technology (ILT) images of target patterns. GAN models, pix2pix, and CycleGAN, were first trained and then utilized to synthesize realistic ILT images. Results: These ILT images were eventually translated to polygons of SRAF with simplification process and mask manufacturing rules check constraints. The simulation results demonstrate that CycleGAN approach can place SRAF with comparable performance to mask optimization (MO) result which was optimized by the Tachyon source-mask optimizer (SMO). Conclusions: Methodologies of SRAF placement based on GANs were demonstrated in this paper. While traditional MO approach showed the best lithography performance, CycleGAN in particular was close behind and had much better performance than conventional RB-SRAF approach, whilst achieving good stability for different layout environments. Most importantly, the efficiency of SRAF insertion can be enhanced significantly through GANs.