Development strategy for the irrigation sector of Sri Lanka 2006-2016

Irrigation management / Development plans / Policy / Operations / Maintenance / Rehabilitation / Investment planning / Watersheds / Institutional development / Sri Lanka / Mahaweli Project

OPC model error study through mask and SEM measurement error

International audienceMask and metrology errors such as SEM (Scanning Electron Microscopy) measurement errors are currently not accounted for when calibrating OPC models. Nevertheless, they can lead to erroneous model parameters therefore causing inaccuracies in the model prediction if these errors are of the same order of magnitude than targeted modeling accuracy. In this study, we used a dedicated design of hundreds of features exposed through a Focus Exposure Matrix (FEM). We measured the mask bias from target for these structures and investigated its impact on the model accuracy. For the metrology error, we compared the SEM measurements to AFM measurements for as much as 105 features exposed in various process conditions of dose and defocus. These data have then been used in a OPC model calibration procedure. We show that the impact of the metrology error is not negligible and demonstrate the importance of taking into account these errors in order to improve the reliability of the OPC models

Microscale adhesion patterns for the precise localization of amoeba

In order to get a better understanding of amoeba-substrate interactions in the processes of cellular adhesion and directional movement, we engineered glass surfaces with defined local adhesion characteristics at a micrometric scale. Amoeba (Dictyostelium dicoideum) is capable to adhere to various surfaces independently of the presence of extracellular matrix proteins. This paper describes the strategy used to create selective adhesion motifs using an appropriate surface chemistry and shows the first results of locally confined amoeba adhesion. The approach is based on the natural ability of Dictyostelium to adhere to various types of surfaces (hydrophilic and hydrophobic) and on its inability to spread on inert surfaces, such as the block copolymer of polyethylene glycol and polypropylene oxide, named Pluronic. We screened diverse alkylsilanes, such as methoxy, chloro and fluoro silanes for their capacity to anchor Pluronic efficiently on a glass surface. Our results demonstrate that hexylmethyldichlorosilane (HMDCS) was the most appropriate silane for the deposition of Pluronic. A complex dependence between the physicochemistry of the silanes and the polyethylene glycol block copolymer deposition was observed. Using this method, we succeed in scaling down the micro-fabrication of pluronic-based adhesion motifs to the amoebaComment: Microelectronic Engineering (2008) in pres

Pengaruh harga dan kualitas produk terhadap keputusan pembelian sepatu Nike : Studi kasus pada konsumen Sepatu Nike di Daerah Patani Thailand

Pada dasarnya konsumen selalu menanyakan harga dan kualitas produk dalam suatu produk di perusahaan, kemudian para konsumen akan memilih harga yang murah dan kualitas produk yang bagus dalam membeli produk. Penelitian ini bertujuan untuk mengetahui besarnya pengaruh Harga dan Kualitas Produk secara simultan terhadap keputusan pembelian sepatu Nike pada konsumen sepatu nike di daerah Patani. Penelitian ini menggunakan metode deskriptif dengan pendekatan kuantitatif. Analisis data yang digunakan adalah uji validitas, uji reliabilitas, analisis regresi linier berganda, analisis korelasi, pengujian hipotesis (Uji t dan uji f), dan analisis determinasi. Uji hipotesis menggunakan taraf nyata 0.05 atau 5%. Pengolahan data dalam penelitian ini menggunakan bantuan Software Statistical Product and Service Solution (SPSS) 20.0 for windows. Hasil penelitian menujukan bahwa secara parsial Harga tidak berpengaruh positif dan signifikan terhadap keputusan pembelian dengan thitung sebesar 0,395 dengan tingkat signifikansi sebesar 0.694. karena nilai signifikan lebih besar dari taraf nyata (0,694 > 0,05) maka Ho diterima dan Ha ditolak. Dan kualitas produk berpengaruh positif dan signifikan terhadap keputusan pembelian dengan thitung sebesar 8,730 dengan tingkat signifikansi sebesar 0,000. Karena nilai signifikan lebih kurang dari taraf nyata (0,000 > 0,05) maka Ho ditolak dan Ha diterima. Hasil penelitian koefisien regresi secara bersama-sama diperoleh Fhitung sebesar 39,381 karena Fhitung > Ftabel (39,381 > 3,090) maka Ho ditolak dan Ha diterima. Jadi dapat disimpulkan bahwa Harga dan Kualitas Produk memiliki pengaruh yang tidak signifikan terhadap keputusan pembelian. Nilai R- square diperoleh dengan nilai sebesar 0,448 mengindikasikan bahwa 44.8% perubahan pada keputusan pembelian dapat dijelaskan oleh variabel-variabel bebas/independen (Harga dan kualitas Produk) yang digunakan dalam penelitian ini. Sedangkan sisanya 56.2% dijelaskan oleh variabel lain yang tidak dimasukkan dalam model regresi

An inverse ellipsometric problem for thin film characterization: comparison of different optimization methods

International audienceIn this paper, an ill-posed inverse ellipsometric problem for thin film characterization is studied. The aim is to determine the thickness, the refractive index and the coefficient of extinction of homogeneous films deposited on a substrate without assuming any a priori knowledge of the dispersion law. Different methods are implemented for the benchmark. The first method considers the spectroscopic ellipsometer as an addition of single wavelength ellipsometers coupled only via the film thickness. The second is an improvement of the first one and uses Tikhonov regularization in order to smooth out the parameter curve. Cross-validation technique is used to determine the best regularization coefficient. The third method consists in a library searching. The aim is to choose the best combination of parameters inside a pre-computed library. In order to be more accurate, we also used multi-angle and multi-thickness measurements combined with the Tikhonov regularization method. This complementary approach is also part of the benchmark. The same polymer resist material is used as the thin film under test, with two different thicknesses and three angles of measurement. The paper discloses the results obtained with these different methods and provides elements for the choice of the most efficient strategy

Outliers detection by fuzzy classification method for model building

International audienceOptical Proximity Correction (OPC) is used in lithography to increase the achievable resolution and pattern transfer fidelity for IC manufacturing. Nowadays, immersion lithography scanners are reaching the limits of optical resolution leading to more and more constraints on OPC models in terms of simulation reliability. The detection of outliers coming from SEM measurements is key in OPC [1]. Indeed, the model reliability is based in a large part on those measurements accuracy and reliability as they belong to the set of data used to calibrate the model. Many approaches were developed for outlier detection by studying the data and their residual errors, using linear or nonlinear regression and standard deviation as a metric [8]. In this paper, we will present a statistical approach for detection of outlier measurements. This approach consists of scanning Critical Dimension (CD) measurements by process conditions using a statistical method based on fuzzy CMean clustering and the used of a covariant distance for checking aberrant values cluster by cluster. We propose to use the Mahalanobis distance [2] in order to improve the discrimination of the outliers when quantifying the similarity within each cluster of the data set. This fuzzy classification method was applied on the SEM CD data collected for the Active layer of a 65 nm half pitch technology. The measurements were acquired through a process window of 25 (dose, defocus) conditions. We were able to detect automatically 15 potential outliers in a data distribution as large as 1500 different CD measurement. We will discuss about these results as well as the advantages and drawbacks of this technique as automatic outliers detection for large data distribution cleaning