Tool-induced hotspot fixing flow for high volume products

The feasibility of Hot Spot Fixing (HSF) system in DfM flow is studied and reported. Hot spot fixing using process simulation is indispensable under low-k1 lithography process for logic devices with advanced design rule (DR). Hot spot such as pinching, bridging, line-end shortening will occur, mainly depending on local pattern context. Proper calibration of DR, mask data preparation (MDP), resolution enhancement technique (RET) and optical proximity effect correction (OPC) will reduce potential hot spots. However, pattern layout variety is so enormous that, even with most careful calibration of every process, unexpected potential hot spots are occasionally left in the design layout 1-2. OPC optimization is useful for maximizing common process margin, but it cannot expand individual pattern's process margin without modification of design layout. So, at an early design stage, hot spot extraction using lithography compliance check (LCC) and manual modification of design at hot spots will be a simple and useful method. The problem is that, it is difficult to determine how to modify layout in order to be consistent with DR, MDP/OPC rule. For proper layout modification, intimate knowledge of the entire process would be necessary, and moreover, the modification work often tends to be iterative, and thus time-consuming. Therefore, using our automated HSF system in the cell design stage and also the chip design stage is helpful for fixing design layout while avoiding fatal hot spot occurrence, with enough process margin and also with short turnaround time (TAT) 3-4. The basic system flow in the developed system is as follows; LCC extracts potential hot spots, and the hot spots are categorized by lithography error mode, grade, and surrounding context. And then, hot spot modification instructor, taking the surrounding situation into consideration, generates modification guide for every hot spot. Design data is automatically modified according to the instruction at every hot spot, complying with the design rule. The design modification process is verified with design-rule checker (DRC) and process simulation to confirm hot spot elimination without side effect. In this work, HSF is implemented in the design flow for various logic devices of 65 nm node. We extend modification target layers to multiple critical layers, including active area, poly, local metal wire and intermediate metal wire. The feasibility of the provided HSF system has been studied by applying it to around one hundred data of various sizes with respect to pattern fixing rate and turn around time (TAT). Moreover, process margin expansion including depth of focus (DOF) and exposure latitude (EL), in small layout was verified using process simulation and also by experimental results, namely, scanning electron microscope (SEM) images of focus exposure matrix. The detailed results are shown in the paper.

Finding the true on-product hot spots (patterning defects) by High Volume Manufacturing (HVM) inspection tools is increasingly challenging as the process window margin shrinks. It is a common practice nowadays to use Optical Rule Check (ORC) results by computation lithography to provide “care areas” to increase the signal to noise level of the inspection tool, thus improving the detection accuracy. The care area defined by the traditional method of contour-based process window checks may not be good enough. There are cases where real yield killers were not caught by contour-based checks, resulting in missing errors during wafer inspection as well. In this paper, we expand the traditional process window checks to a broader lithographic spectrum. The method allows us to utilize additional limiters such as max intensity, contrast, and NILS checks in combination with normal CD-based checks such as bridge, pinch, or process window bands to achieve higher accuracy in failure locations. This compound check will be trained using existing on product failure data obtained from low and high resolution wafer inspection as well as eTest and yield data. The combination of contour and intensity-based checks is demonstrated to be more effective in capturing the wafer hot spots for new products. The various usage models of such enhanced ORC will also be discussed.

As minimum feature sizes shrink, unexpected hotspots appear on wafers. Therefore, it is important to detect and fix these hotspots at design stage to reduce development time and manufacturing cost. Currently, as the most accurate approach, lithography simulation is widely used to detect such hotspots. However, it is known to be time-consuming. This paper proposes a novel aerial image synthesizing method using regression and minimum lithography simulation for only hotspot detection. Experimental results show hotspot detection on the proposed method is equivalent compared with the results on the conventional hotspot detection method which uses only lithography simulation with much less computational cost.

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.

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>

以西北太平洋（150°E-160°E、38°N-45°N）柔鱼<em>Ommastrephes bartramii</em>为研究对象，以2007和2010年鱿钓渔业的原始点位数据为基础，利用常规统计和探索性空间数据分析（ESDA）中的全局空间自相关分析方法，对西北太平洋柔鱼资源空间分布及其变动进行了研究。结果表明，西北太平洋柔鱼资源呈现较强的聚集分布特征，但不同空间位置差异较大。利用GIS和ESDA的局部自相关方法进行了柔鱼资源热冷点空间分布的制图，结果显示2007年研究区存在3个显著的热点和1个冷点，2010年与之存在较为明显的差异，具有1个热点和4个冷点。变化检测分析表明，两个年份间出现1个热点区域和1个冷点区域未发生变化，另有2个区域从2007年热点变成2010年冷点；此外，非热冷点之间的变动在研究区占据了主导地位。景观指数分析表明，研究区柔鱼资源热冷点格局的复杂性和自相似性并不高，但其异质性和聚集性非常强，且整体性和凝聚度均很高。分析认为，2007年西北太平洋柔鱼渔区的形成受温度和海流的影响，近一半作业渔区产量相对较高，但并未充分保证空间热点的大面积形成，空间热点和冷点分布面积大致相当；2010年整个西北太平洋柔鱼渔场受亲潮势力影响，空间热点较为集中、空间冷点较多且分散。;The oceanic cephalopod <em>Ommastrephes bartramii</em> is widely distributed in the northwestern Pacific Ocean and is an important target species for the fishing industries of Japan, South Korea, China and Chinese Taiwan. Owing to the effects of global climate change and increased fishing activities, the spatial patterns and hot spot dynamics of <em>O. bartramii </em>resources are changing. This study focused on <em>O. bartramii</em> resources, especially within the boundaries of 150°E-160°E and 38°N-45°N, in the northwestern Pacific Ocean. Based on original fishing data from Chinese boats, the study aimed to explore the spatial structure of <em>O. bartramii</em> resources using integrated methods of spatial statistics, global spatial autocorrelation, change detection and landscape metrics. Classical statistics and global spatial autocorrelation-based primary analysis showed that there were vast differences in the resource abundance of <em>O. bartramii </em>over the study area. A relatively strong aggregated distribution was observed for <em>O. bartramii</em> in the northwestern Pacific Ocean. In addition, spatial hot and cold spot maps were produced for the years of 2007 and 2010 using both a geographic information system and local spatial autocorrelation methods. The maps illustrated that there were three obvious hot spots and one cold spot for <em>O. bartramii</em> in the northwestern Pacific Ocean in 2007, whereas in 2010 there was one obvious hot spot and four cold spots. Change detection demonstrated that only one spatial hot spot remained unchanged between 2007 and 2010, while two areas showed a change in status from hot to cold spots. In addition, only one of the cold spots remained unchanged between 2007 and 2010. As a result, the non-hot and non-cold spots that showed non-aggregated or random distributions dominated the pattern of <em>O. bartramii</em> in the northwestern Pacific Ocean. Landscape metrics were also employed in this study to mine the implicit information and knowledge of hot/cold spots of <em>O. bartramii</em> resources. This research demonstrated that relatively low complexity and self-similarity were observed for the <em>O. bartramii</em> resource, while strong spatial heterogeneity and aggregation, as well as high integrity and cohesion, were detected for the same fishery resource. Finally, a sea surface temperature-based analysis revealed the marine environmental characteristics and dynamics of spatial hot/cold spots of <em>O. bartramii</em> in the northwestern Pacific Ocean in both 2007 and 2010. The results showed that, owing to the influence of sea surface temperature and currents within the fishing area of the northwestern Pacific Ocean, approximately half of the area has relatively high production. However, high production alone cannot account for the hot spot clustering within most of the study area. Hence, in 2007, only 7.0% of the study area was classified as hot spots, while most of the area comprised cold spots or areas that were not statistically significant. As a result of the Oyashio Current, the fishing grounds distributed at 40°-45°N and 154°-158°E during the best fishing period displayed relatively aggregated spatial hot spots in 2010, whereas the spatial cold spots were scattered across the northwestern Pacific Ocean.

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.

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.

Although a number of factors relating to lithography and material stacking have been investigated to realize hotspot-free wafer images, hotspots are often still found on wafers. For the 22-nm technology node and beyond, the detection and repair of hotspots with lithography simulation models is extremely time-consuming. Thus, hotspots represent a critical problem that not only causes delays to process development but also represents lost business opportunities. In order to solve the time-consumption problem of hotspots, this paper proposes a new method of hotspot prevention and detection using an image recognition technique based on higher-order local autocorrelation, which is adopted to extract geometrical features from a layout pattern. To prevent hotspots, our method can generate proper verification patterns to cover the pattern variations within a chip layout to optimize the lithography conditions. Moreover, our method can realize fast hotspot detection without lithography simulation models. Obtained experimental results for hotspot prevention indicated excellent performance in terms of the similarity between generated proposed patterns and the original chip layout patterns, both geometrically and optically. Moreover, the proposed hotspot detection method could achieve turn-around time reductions of >95% for just one CPU, compared to the conventional simulation-based approach, without accuracy losses.

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.

Layout hotpot detection is one of the main steps in modern very-large-scale-integration (VLSI) chip design. A typical hotspot detection flow is extremely time consuming due to the computationally expensive mask optimization and lithographic simulation. Recent researches try to facilitate the procedure with a reduced flow, including feature extraction, training set generation, and hotspot detection, where feature extraction methods and hotspot detection engines are deeply studied. However, the performance of hotspot detectors relies highly on the quality of reference layout libraries which are costly to obtain and usually predetermined or randomly sampled in previous works. In this article, we propose an active learning-based layout pattern sampling and hotspot detection flow, which simultaneously optimizes the machine-learning model and the training set that aims to achieve similar or better hotspot detection performance with much smaller number of training instances. Experimental results show that our proposed method can significantly reduce lithography simulation overhead while attaining satisfactory detection accuracy on designs under both DUV and EUV lithography technologies.

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.

BackgroundDengue is an acute arboviral disease responsible for most of the illness and death in tropical and subtropical regions. Over the last 25 years there has been increase epidemic activity of the disease in the Caribbean, with the co-circulation of multiple serotypes. An understanding of the space and time dynamics of dengue could provide health agencies with important clues for reducing its impact.MethodsDengue Haemorrhagic Fever (DHF) cases observed for the period 1998–2004 were georeferenced using Geographic Information System software. Spatial clustering was calculated for individual years and for the entire study period using the Nearest Neighbor Index. Space and time interaction between DHF cases was determined using the Knox Test while the Nearest Neighbor Hierarchical method was used to extract DHF hot spots. All space and time distances calculated were validated using the Pearson r significance test.ResultsResults shows that (1) a decrease in mean distance between DHF cases correlates with activity leading up to an outbreak, (2) a decrease in temporal distance between DHF cases leads to increased geographic spread of the disease, with an outbreak occurrence about every 2 years, and (3) a general pattern in the movement of dengue incidents from more rural to urban settings leading up to an outbreak with hotspot areas associated with transportation hubs in Trinidad.ConclusionConsidering only the spatial dimension of the disease, results suggest that DHF cases become more concentrated leading up to an outbreak. However, with the additional consideration of time, results suggest that when an outbreak occurs incidents occur more rapidly in time leading to a parallel increase in the rate of distribution of the disease across space. The results of this study can be used by public health officers to help visualize and understand the spatial and temporal patterns of dengue, and to prepare warnings for the public. Dengue space-time patterns and hotspot detection will provide useful information to support public health officers in their efforts to control and predict dengue spread over critical hotspots allowing better allocation of resources.

As the litho hotspot detection runtime is currently in a continuous increase with sub-10nm technology nodes due to the increase of the design and process complexity, new methods and approaches are needed to improve the runtime while guaranteeing high accuracy rate. Machine-Learning Fast LFD (ML-FLFD) is a new flow that uses a specialized machine learning technique to provide fast and accurate litho hotspot detection. This methodology is based on having input data to train the machine learning model during the model preparation phase. Current ML-FLFD techniques depend on collecting hotspots (HS) and Non hotspots (NHS) data from the drawn layer in order to train the model. In this paper, we present a new technique where we use the retarget data to train the machine learning model instead of using the drawn hotspot data. Using retargeting data is getting one step closer to the actual printed contours which gives a better insight about the hotspots of the manufactured wires during the machine learning model training step. The effect of using closer data to the printed contours will be reflected on both the accuracy and the extra rate which will reduce simulation area. In the different sections of this paper, we will compare the new approach of using retarget data as a ML input to the current technique of using drawn data. Pros and cons of the two approaches will be listed in details including the experimental results of hotspot accuracy and litho simulation area.