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Focus spot reduction by brush scrubber cleaning Antoine Pacco1, a, Els Kesters1, Ihsan Simms2, Kathleen Nafus2, Jelle Vandereyken1, Hiroki Yonekawa2 1 imec, Kapeldreef 75, B-3001 Leuven, Belgium 2 Tokyo Electron Limited, 1-1 Fukuhara, Koshi City, Kumamoto Japan 861-1116 aantoine.pacco@imec.be Keywords: Scrubber, Brush, Backside Cleaning, Backside Contamination, Focus Spots, Chuck Spots, Hot Spots, Overlay, Immersion, EUV Introduction As immersion lithography extends into the 22nm node and beyond through the use of multiple and other advanced patterning techniques, the requirements for overlay, CDU and focus deviation reduction also become tighter [1].
In this study that was performed at imec’s 300mm cleanroom facility, the study objective was to evaluate the focus spot reduction performance of a stand-alone scrubber in a case study featuring known focus spot generating equipment sets. .
This backside contaminated test material was used in our cleaning experiments and evaluation of focus spot reduction by performing leveling tests on an immersion scanner that was able to measure out of plane deviations.
The standard sensitivity of the measurement for the out of plane deviation on the scanner was 50nm but some data was obtained with a sensitivity till 30nm.
A reduction of focus spots is visible after application of the PVA brush.

Enchanced Expression of Gene Data Mrs.
A.Clustering high-dimensional data.
Feature subset selection is used for data reduction by removing redundant dimension.Subspace clustering is based on observation that different subspaces may contain different meaningful clusters.It searches group of clusters within different subspaces of same data set.Three techniques for effective clustering of high-dimensional data are:-dimension-growth subspace clustering (CLIQUE),dimension-reduction projected clustering(PROCLUS) & frequent pattern-based clustering(pCluster).
Authomatic subspace clustering of high dimensional data for data mining applications.
Raghavan, “Automatic subspace clustering of high dimensional data for data mining applications”, Proc.

The method of liquid reduction on solutions is most used for obtaining only nanopowders of metals (copper, silver, nickel) with low values of the reduction potential.
This color of the solution was shown the production of copper nanoparticles as a result of the reduction reaction.
Ren, A chemical reduction approach to the synthesis of copper nanoparticles, Int Nano Lett, (2016) 21-26
Mirzaeva, Analytical solution of the problem of diffusional transformation under continuous cooling condition based on isothermal transformation diagram data, Materials Performance and Characterization, 2(1) (2013) 134-152
Kirsanova, Liquid-phase reduction of steelmaking wastes, Metallurgist. 59 (2016) 1024-1029

Later the models have been validated against experimental data as reported in the previous studies.
By utilising the finite element (FE) method, a detailed investigation of the load data for different interface boundary conditions during ultrasonic forming can be carried out.
The predicted necking force-plunger displacement curves were derived from the FE data.
The simulations were completed when l was 21.4 mm and the buckling profiles of the necked thin cylinder were plotted from the field data of the FE models.
This calculated data agrees with a previous experimental study [12] which reported that by superimposing a radial ultrasonic excitation on the die the mean necking force was significantly reduced, in this case to 35 to 45 % of the static force.

Therefore, considerable impetus aimed to develop novel and efficient technologies for excess sludge yield reduction.
The study of TCS showed, when the concentration of TCS is 0.8-1.0mg/L, the sludge reduction rate was above 40% [1].
However, there have been few investigations on excess sludge reduction by combined metabolic uncouplers.
In order to obtain the daily sludge growth data, MLSS, COD, NH3-N, and sludge volume index (SVI) were measured according to (APHA, 2005).
This founding revealed a feasibility of applying a combined uncoupler to achieve the reduction of excess sludge in SBR.

This study is aimed at the statistical survey of lightweight steel structures for industrial and residential use (current status): using data from project investigations to present the site overview and hazard risk factors.
Risk assessment for buildings with accommodation inside the factory (validation): through the statistical survey data (34 of the 188 surveyed have accommodations inside the factory, a ratio of 18.1%), the hazard risk is assessed, and the potential risks are analyzed.
The presentation of the data shows the proportion of high fire risk in violation of the iron factory.
Use the factory diagnostic data to analyze feedback, understand the fire risk distribution, to grasp the key areas, and follow-up fire prevention and propaganda and disaster rescue war chess deduction, and to give suggestions to the site by the building structure and decoration materials.
Smokeview, A Tool for Visualizing Fire Dynamics Simulation Data Volume I: User's Guide, NIST Sp.

Li3V2(PO4)3/C has been synthesized by a simple one-step carbothermal reduction technology using LiH2PO4, V2O5 as the raw materials and sucrose as carbon source.
X-ray diffraction (XRD) and scanning electron microscope (SEM) measurements showed that Li3V2(PO4)3/C synthesized by one-step carbothermal reduction had monoclinic structure.
Carbon coated Li3V2(PO4)3 have been prepared using various methods,such as hydrogen reduction method [7, 14, 17-18], carbothermal reduction method(CTR) [3, 8, 16, 19], microwave method [20-21], sol-gel [8, 19, 22-24] , rheological phase method [25].
The impedance data were recorded in an AC voltage of 5mV in the frequency range from 100kHz to 1 mHz.
Fig.12 The CV(a) and EIS (b) of the optiminized Li3V2(PO4)3 /C Conclusions We have prepared Li3V2(PO4)3/C by a simple one-step carbothermal reduction technology.

Data analysis was carried out with Vmap [13], and the mean of the three measurement areas is reported, with the minimum and maximum value around the mean shown as the uncertainty.
The grain size development is shown with labeled data points, and the data for low-strain (5%) processing are underlined. (5% processed samples - open symbols; 15% processed samples - closed symbols).
Label (A) highlights data after a reduction of 15% with short annealing times.
Each data point derives from one area with dimensions of at least 500 µm x 500 µm. (5% processed samples - open symbols; 15% processed samples - closed symbols).
Data are shown separately for the clusters of Σ3 boundaries, Σ3-related variant grain boundaries (Σ9, Σ27) and clusters of Σ3, Σ9 and Σ27 boundaries (i.e. all Σ3 n (1≤ n ≤3)).

The shear strength is reduced with the dissipation of effective stress, the reduction is just the research contents of unsaturated soil.
On the values of , Khalili and other scholars deduced an approximate expression of the parameter by fitting analysis of experimental data[2].
Soil strength reduction method.
The analysis shows that the results of two stability safety factor differs little, thus the feasibility of this strength reduction method was proved.
(the black line in Fig. 4 is hierarchical indicate line of the slope ) The soil is divided into four layers, then put the corresponding data of table 1 into analysis software, and obtained the slope stability factor with this soil strength reduction method , the result is 1.4325 in this paper. shear plastic zone of the slope is shown in Fig.5.

The rough set theory deals with identifying structural relationships in the data and it is useful in discovering potentially significant facts or data patterns in multidimensional attribute collections.
In a word, RSBPNN model through rough set to do data reduction uses the dataset as the design basis and training data of BP neural network, thus the two methods can complement each other.
This paper adopts the data from 2001 to 2006 as the training sample, the data have been disposed by tab.1.
“Equal frequency binning” in the ROSETTA software is used to do the data discrimination, and The Johnson's algorithm in the ROSETTA software is used to do attribute reduction.
[5] Ivo Diintsch, GUnther Gediga, “Rough set data analysis.”