Combinational optical rule check on hotspot detection

Flow of fixing of hot spot induced by optical variation among exposure tools is discussed for quick ramp-up of high volume products. To achieve robust pattern formation for optical variation, following hot spot detection and fixing approaches are introduced: i) at the design stage, hot spot detection within the optical variation space and hot spot fixing by layout modification or OPC optimization, ii) in order to efficiently detect hot spots within the optical variation space, lithography simulation by combinations of optical parameters determined by the design of experiment (DoE), iii) at the manufacturing stage, hot spot fixing by adjustment of optical parameters using the multi-variable optimization to match OPE between the primary and secondary exposure tool.

As the semiconductor industry enters the 45nm node and beyond, the tolerable lithography process window significantly shrinks due to the decreasing k₁ factor and increasing lens NA required to meet product shrink goals. The usable depth of focus at the 45nm node for critical layer is less than 200nm and for the 32nm node it will approach 100nm. Consequently, process window aware Optical Proximity Correction (OPC) and Lithography Rule Check (LRC) become crucial to ensure the robustness of OPC to focus and dose variation. An accurately calibrated continuous process window model is the corner stone for successful process variation aware OPC and LRC. For ease of use, this calibrated model should be a continuous function of defocus and dose and able to interpolate and extrapolate in the usable process window. Lithographic proximity effects have an optical component and a resist component. As state of the art OPC simulation tool is capable of precise and fast optical simulation, however its treatment of chemical amplified resist effects is relatively crude and does not capture the complex behavior during acid & quencher reaction, diffusion and development. This in turn causes difficulties for a continuous process window model where the resist component plays an important role. We proposed a hybrid resist model, which is a superposition of a traditional OPC chemical amplified resist model and a first order resist bias model. Using Synopsys' OPC modeling software package-ProGen, we incorporated this hybrid resist model into the continuous process window (PW) modeling module, and very good model calibration performance was achieved.

The lithography industry has historically striven to improve resolution by reducing wavelength and increasing the lens’ numerical aperture (NA). The introduction of 0.33 NA extreme ultraviolet (EUV) lithography into high-volume manufacturing (HVM) represents the largest jump in resolution ever achieved by the industry. However, even this resolution is not sufficient for the patterns required for beyond the 2 nm logic technology node. This is due to low contrast and the diffraction limit of current EUVL scanners for the mask patterns required for these nodes. Instead, the resolution must be improved by increasing the NA. This will also increase the contrast of patterns which had insufficient contrast at 0.33 NA, which will in turn improve LCDU and defectivity. This change is not without its challenges though. Increasing the NA from 0.33 to 0.55 will cause a significant reduction in depth of focus. In addition, stronger mask 3D effects can cause pattern dependent shifts in best focus. As a result, the common overlapping process window of several critical patterns can become strongly diminished. The use of anamorphic optics will require two separate half-field exposures to obtain the equivalent of a single full-field exposure on current EUV and DUV scanners. For some chip sizes, this will require stitching two half-fields together to pattern the full chip area. In previous technology nodes, the process window could be improved using SMO and SRAFs. In addition, over the last five years, the industry has put significant effort into studying alternative absorbing materials. These materials can significantly reduce the mask 3D effects by reducing the thickness of the absorber. The use of alternative absorbers alone will not be sufficient for improving the overlapping process window. Instead, several techniques must be simultaneously utilized in order to ensure sufficient overall process window. Optimization of overlapping process windows is critical for successful insertion of high-NA EUVL into HVM. In this paper we analyze how the process window of critical patterns can be optimized by using different optimizations. We will show for realistic mask designs how process window can be improved in different process steps. Double exposure from half-field stitching will also be included in the process evaluation. We use both rigorous and compact modeling in a complimentary fashion for overall process optimization analysis. All techniques presented in this paper accurately model the anamorphic, centrally obscured optics of the upcoming next-generation high-NA scanners.

Hot spots are residues comprising only a small fraction of interfaces yet accounting for the majority of the binding energy. These residues are critical in understanding the principles of protein interactions. Experimental studies like alanine scanning mutagenesis require significant effort; therefore, there is a need for computational methods to predict hot spots in protein interfaces. We present a new intuitive efficient method to determine computational hot spots based on conservation (C), solvent accessibility [accessible surface area (ASA)] and statistical pairwise residue potentials (PP) of the interface residues. Combination of these features is examined in a comprehensive way to study their effect in hot spot detection. The predicted hot spots are observed to match with the experimental hot spots with an accuracy of 70% and a precision of 64% in Alanine Scanning Energetics Database (ASEdb), and accuracy of 70% and a precision of 73% in Binding Interface Database (BID). Several machine learning methods are also applied to predict hot spots. Performance of our empirical approach exceeds learning-based methods and other existing hot spot prediction methods. Residue occlusion from solvent in the complexes and pairwise potentials are found to be the main discriminative features in hot spot prediction. Our empirical method is a simple approach in hot spot prediction yet with its high accuracy and computational effectiveness. We believe that this method provides insights for the researchers working on characterization of protein binding sites and design of specific therapeutic agents for protein interactions. The list of training and test sets are available as Supplementary Data at http://prism.ccbb.ku.edu.tr/hotpoint/supplement.doc. Supplementary data are available at Bioinformatics online.

At the 28nm technology node and below, hot spot prediction and process window control across production wafers have become increasingly critical. We establish proof off concept for ASML’s holistic lithography hot spot detection and defect monitoring flow, process window optimizer (PPWO), for a 228nm metal layer process. We demonstrate prediction and verification of defect occurrence on wafer that arise from focus variations exceeding process window margins of device hotspots. We also estimate the improvement potential if design aware scanner control was applied.

In wireless sensor networks, sensor nodes are capable of not only measuring real world phenomena, but also storing, processing, and transferring these measurements. Many techniques have been proposed for storing and retrieving data in these networks. These methods are classified into three main categories. Data-centric storage is the most important one. The name comes from the fact that data's storage location is determined according to it's event type. However, most of the methods proposed in this category use a fixed event-location mapping or don't take nodes' resources into consideration. Therefore, storage nodes may experience unbalanced resource utilization problems which results into hot spot areas. We address this problem by proposing a dynamic mapping scheme which aims to reach two goals: First, to prolong network lifetime by efficient energy consumption and second, increasing quality of service by increasing data availability. The proposed method comes in two phases: First hot spot detection phase where storage nodes' status information is gathered at sink. Applying the proposed hot spot detection mechanism, sink detects a hot spotted node and alarms the node by sending a warning message to it. The hot spotted storage node receives the warning message and triggers the second phase named hot spot mitigation. In this stage it should transfer the responsibility of storing the event to another node in its' h hop neighborhood. Two metrics are considered in selecting a new storage node: 1) minimizing the total energy consumed for storing and retrieving the event 2) distributing network load evenly. The simulation presented here evaluates the proposed method in terms of network lifetime, data availability and energy consumption. The results show that the proposed method will delay hot spot effect with acceptable overhead.

<p>Io is the Solar System body showing the largest number of active volcanic centers, primarily generated by tidal heating [1]. Many ground based and remote sensing observations have revealed spatial and temporal variabilities, important to define the characteristics of tidal heating and the mechanisms by which heat escapes from the interior [2, 3, 4].</p> <p>The Jovian Infrared Auroral Mapper (JIRAM), the imager/spectrometer onboard JUNO mission, mainly devoted to the study of Jupiter’s atmosphere, had the opportunity to acquire data of the Galilean satellites, including Io. JIRAM is composed of a slit spectrometer covering a range of wavelengths between 2 and 5 μm and an imager equipped with two filters, L and M, centered at 3.4 and 4.7 μm, respectively [1]. </p> <p>In this work, we mapped the Io hot spots by using JIRAM M filter images from orbits 7, 9, 10, 16, 17, 18, 20, 24, 25, 26, 27, 32, 33, with a spatial scale ranging from ∼ 48 up to ~150 km/pixel (Fig. 1), updating the work by [5]. For each orbit, we consider a set of images (referred to as “super images”) which contain the average radiance of several JIRAM contiguous observations. This approach minimizes the effects due to spurious pixels, reducing the detection of false hot spots [5].</p> <p>JIRAM data cover almost the entire surface of Io, with a redundancy up to 43 super images per pixel in the northern and equatorial regions (Fig. 2), improving the observations of the polar areas. To identify Io hot spots, we first filtered each super image for emission angles > 75°. Then, since JIRAM data have been acquired at different incidence angles, including Io dayside, nightside, and eclipse (Fig. 1), to select the hot spots, we divided the images in three categories: day (i≤ 70°), dawn/dusk (70° < i < 90°) and night i≥ 90°. For each super image, we calculated the median value of the radiance for the dayside, nightside and dawn/dusk background. Hence, we considered as hot spots’ detection limit the radiance values larger than the median radiance for the night side, and median background radiance plus 0.003 for dawn/dusk and day side. </p> <p>We identified 242 hot spots, many of which had already been included in other databases (e. g. [6, 7, 8, 9]). Among the hot spots revealed by JIRAM, 24 have not been observed before, and half of these are located at the poles.  The comparison between our results and the last Io global heat flow power output map published by [8] suggests that the highest power heat flow hot spots are still active, indicating their activity lasts for decades or more, while a large part of the intermediate power heat flow hot spots are still present, as only 15 of them are not included in our map. We found the larger discrepancies in the lower heat flow hot spots, given that many of those listed as active by [8] have not been observed by JIRAM. </p> <p>The study of Io hot spots variation and distribution, is worthwhile in helping to constrain the interior models and the magma distribution in its subsurface. The Io hot spots map presented here is the most up-to-date one produced by remote sensing datasets. The future JIRAM higher spatial resolution observations will be fundamental to extend and confirm our results, better distinguish close hot spots and improve our overall knowledge of Io volcanic processes and evolution.</p> <p><img src="" alt="" /></p> <p><strong>Figure 1: </strong>Example of Io JIRAM M filter images for the orbits JM 24 and 25.</p> <p><img src="" alt="" /></p> <p><strong>Figure 2: </strong>JIRAM M filter data coverage of Io for JIRAM orbits 10, 11, 16, 17, 18, 20, 24, 25, 26, 27, 32, 33.</p> <p><strong>Acknowledgments</strong></p> <p>This work is supported by Agenzia Spaziale Italiana (ASI). JIRAM is funded by ASI contract 2016-353 23-H.0. </p> <p><strong>References</strong></p> <p>[1] Peale, S. J.,  et al., 1979, Science, 203. [2] Davies et al., 2015, Icarus, 262. [3] de Kleer et al., 2019, The Astronomical Journal, 158. [4] Adriani et al., 2017, Space Science Review, 213. [5] Mura, A., et al., 2020, Icarus, 241. [6] Lopes and Spencer, Eds, 2007, Praxis-Springer. [7] Williams, D. A., et al., 2011, Icarus, 214. [8] Veeder, G. J., et al., 2015, Icarus, 245. [9] Cantrall, C., et al., 2018, Icarus, 312.</p>

At advanced node of metal layer, defocus conditions need to be taken into account in OPC in order to guarantee metal layer process window. Process window OPC (PWOPC) must be considered for the model-based OPC models under special conditions within a lithographic process window. In this paper, we studied several OPC simulation issues in the application of PWOPC in metal layer. The test run results showed that preparatory work and parameters setting were very important for PWOPC application. Without preparatory work or unbefitting parameters setting, the OPC simulation may lead to undesired results. The depth of focus (DOF) of several hot spots was examined. The pinch and bridge error are hot spots while only OPC model under nominal conditions is employed. These hot spots can be resolved while PWOPC models are in consideration. In addition of PWOPC precision study, PWOPC runtime is also evaluated. The use of PWOPC extends OPC runtime at least 50%, which is unacceptable in the environment of longer OPC runtime for the advanced tech node and continuing pull-in tapeout schedule. The runtime speeding up method is studied to reduce the total runtime.

With constant push for smaller and faster devices photo mask technology has become the most critical part of the entire integrated circuit (IC) production flow. Mask inspection and mask defect repair are increasingly important components of advanced photo mask technology. The low cost of mask manufacturing and the necessity of delivering photo masks to production floor in the shortest possible time require new photo mask specs and acceptance criteria. It is no longer economically viable to reject a photo mask because some mask anomalies were found, or repair all the defects detected by state of the art inspection tool. One should use a smart approach to separate tolerable mask anomalies from real mask defects that might negatively affect device yield. However, this is not a trivial task. With rising mask complexity (e.g., binary masks with aggressive optical proximity correction or phase-shifting masks (PSM) attenuated and alternating) and inspection and metrology tools running out of steam, new technologies such as the AIMSTM and Virtual Stepper system must be used to sort nuisance mask defects from real ones. This will help to reduce the number of required defect repairs and shorten mask manufacturing cycle time. However, it is very difficult to utilize AIMS in the production environment because of its low operational speed; the Virtual Stepper software, in its turn, does rely on mask data captured by inspection/metrology hardware. In the case of phase masks, such as Attenuated PSM (especially high transmission EAPSM) and Alternating PSM, inspection tools are not able to accurately retrieve optical properties of mask materials and as a result defect analysis is becoming very difficult and is an unreliable task. A very common type of PSM defect that occur during mask manufacturing and repair processes are the so-called distributed defects such as gallium stains, riverbed pinhole clusters, and large chrome residuals (on EAPSM). It is very difficult to get accurate information about the transmittance and phase of these defects at actinic wavelength using inspection and metrology tools. With a simulation study one can reconstruct such mask defects, and by varying defect phase and transmission one can learn about the impact of such mask defects on printed wafers. In addition, lithography simulation helps to better understand how mask defects behave under different lithography process conditions. In our work we investigated several cases of distributed mask defects such as large chrome residuals and clustered pinholes on EAPSM. Based on our simulation, we found a relationship between photo mask defect transmissivity and resulting printed wafer critical dimension (CD) error. Simulated wafer results are presented in different forms: CD errors, process window, and Exposure- Defocus latitude changes. Conclusions about distributed defects printability have been made.

The key challenge before EUVL is to make defect-free masks, for which it is important to identify the root cause of defects, and it is also necessary to establish suitable critical mask defect size for the production of ULSI devices. Selete has been developing EUV mask infrastructures such as a full-field actinic blank inspection tool and 199nm wavelength patterned mask inspection tool in order to support blank/mask supplier in reducing blank/mask defects which impact on wafer printing. In this paper, by evaluating the printability of programmed phase defects and absorber defects exposed by full-field scanner EUV1, we demonstrate that defect detection sensitivities of ABI (actinic blank inspection) and PI (patterned mask inspection) are higher than that of WI (wafer inspection) in HP32nm. The evaluations were done by comparing the detection sensitivities of full-field actinic blank inspection tool, 199nm wavelength patterned mask inspection tool, and wafer EB inspection tool. And then, based on the native defect analysis of blank/mask, we ascertained that actinic blank inspection and patterned mask inspection developed at Selete, are effective in detecting killer defects both at the main pattern and at light-shield border area.

With the application and development of photovoltaic (PV) power generation, defects in its operation can significantly reduce the efficiency of power generation, so infrared thermography is crucial for hot spot defect detection in PV modules. In this paper, an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) algorithm is proposed for automatic hot spot detection in aerial infrared images. First, the anchor frame size configuration is optimized based on the clustering algorithm to address the challenges of small hot spot target size and complex background. Subsequently, the ResNet-50 backbone network is used instead of the original network to enhance the hot spot feature extraction capability. Finally, it is trained and tested on the self-constructed thermal image dataset, and the experimental results show that the detection accuracy of this method is improved compared with the traditional method, which provides technical support for the intelligent operation and maintenance of PV on the UAV edge computing platform.

ABSTRACTA new algorithm (FYVOLC) for the automated detection of volcanic hot spots using data obtained from China’s new-generation satellite FY-4A is developed and tested. FYVOLC improves the detection ability of the Volcanic Anomaly SofTware (VAST) algorithm by incorporating a Normalized Brightness Temperature Difference Index (NBTDI) to reduce the influence of ‘cold’ cloud. In addition, FYVOLC introduces a mid-infrared brightness temperature criterion to identify volcanic hot spots by making calculations based on the image itself without artificially determining any parameters. To test the volcanic hot-spot detection performance of FYVOLC, FY-4A Advanced Geostationary Radiation Imager data were used for eruptions from four volcanoes: Mayon Volcano in the Philippines (25–26 January 2018), and Bromo (1–2 September 2018), Lawu (3–4 September 2018), and Soputan volcanoes (3–4 October 2018) in Indonesia. A total of 147 images of the above four volcanoes were used, and the results obtained using the FYVOLC algorithm were compared with those from three existing volcanic hot-spot detection algorithms: the simplified contextual, VAST, and HOTSAT algorithms. It is shown that the simplified contextual and VAST algorithms are prone to generating false alerts (with a maximum false alert rate of up to 41% and 48%, respectively), whereas the simplified contextual and HOTSAT algorithms are prone to missing hot-spot pixels (with a maximum miss rate of up to 71% and 54%, respectively). The FYVOLC algorithm has the best detection accuracy owing to the adopted NBTDI and image-based mid-infrared brightness temperature criterion. The maximum false alert rate of FYVOLC is 12%, and the maximum miss rate is 11%. By analysing the thermal anomaly time-series of the 147 images, it was found that the detection results of FYVOLC are basically consistent with the actual hot spots, except for some images that were strongly affected by cloud cover. This study is the first to realize the automated detection of volcanic hot spots and monitor temporally dynamic thermal phenomena based on FY-4A satellite data. The results have significance for the continuing development of global volcanic early-warning systems and for the dynamic monitoring of volcanoes after eruptions.

Extreme-UV lithography (EUVL) is the most promising candidate of next generation technology for hp20nm node device manufacturing and beyond, however there are many critical issues to solve in the light source, tools, mask and photo resist. Regarding the development of a photo resist, it is necessary for high volume manufacturing (HVM) to improve LWR, resolution limit and sensitivity. Additionally, concerning about deterioration of a patterning performance by Out-of-Band (OoB) light existing in the EUV light, and contamination problem of exposure tool due to the photo resist outgassing are the key issues which have to be resolved toward HVM by EUVL. Especially, the outgassing problem can become a significant issue for fine patterning under high dose exposure condition. This paper proposes the novel solution for these critical issues with the application of a top coat material which is named OBPL (Outgassing & out-of-Band Protection Layer) on photo resist. The key characteristics of OBPL material are to have a role in protection against the OoB adverse effect to keep up the photo resist performance, to suppress the outgassing from photo resist as a barrier layer and to enhance the lithographic performance such as photo resist profile and process window. In designing the OBPL material, the optical property needs having not only the high absorbance of DUV (Deep-UV) light in OoB range but also high transmittance for 13.5nm wavelength to prevent the sensitivity loss. Furthermore, it is found that the polymer backbone affects the outgassing barrier capability in previous evaluation. Based on these investigations, a state-of-the-art OBPL achieves quite a positive lithographic result with sufficient OoB absorption and outgassing suppression. Moreover, this material has applicability to all types of photo resist including NTD (Negative-Tone Development) process. This paper describes the result of the feasibility study for OBPL and the lithography performance with EUV full field scanner.

We present an improved liquid crystal microthermography technique for the detection of defects in large area field emission cathode arrays. Defects can cause electrical leakage paths, leading to localized heating at the defect location. The thermal gradients at these hot spots can be detected by liquid crystal microthermography, but, the direct placement of the liquid crystal is a two step process. First the liquid crystal is deposited by spin coating the device and the liquid crystal has to be removed from the device after the test. This process, apart from being time consuming, can lead to contamination of the test device. In this article, we present an improved liquid crystal microthermography technique for the detection of hot spots in field emitter arrays. The improvement is obtained by hermetically sealing the liquid crystal material inside a packaged assembly made from a glass support and a thin plastic membrane. We have used the new method for the detection of hot spots in large area field emission cathode arrays. This technique provides accurate detection of hot spot locations caused by leakage currents as low as 100 μA.

Human neurocysticercosis is an important parasitic disease in developing countries. Most epidemiological studies on the disease have used antibody-based assays that allow the detection of transmission 'hot spots' and the identification of the main risk factors for transmission. However, such assays have low predictive value in the detection of active cases of neurocysticercosis. The screening potential of the most commonly used antibody-detection technique, the electroimmunotransfer blot assay (EITB), has now been compared with an antigen-capture assay, in an endemic region of Mexico. The subjects were 68 patients with late-onset epilepsy, 35 cases of taeniasis and a randomly selected, control group of 133 individuals from the same region. Parasite-specific antibodies and antigens were more common among the epileptics and taeniasis cases than among the controls. The antigens appeared to be associated with late-onset epilepsy and the antibodies with the presence of subcutaneous nodules. The sensitivities of both tests, to detect epilepsy or taeniasis, were low, but the specificity and the positive predictive value of the antigen-capture assay was high when used with the epileptics. As late-onset epilepsy and neurocysticercosis seem to be associated in endemic regions, antigen-capture assays are probably the most reliable method of detecting active cases of neurocysticercosis in epidemiological studies.