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Institution

Agilent Technologies

CompanySanta Clara, California, United States
About: Agilent Technologies is a company organization based out in Santa Clara, California, United States. It is known for research contribution in the topics: Signal & Mass spectrometry. The organization has 7398 authors who have published 11518 publications receiving 262410 citations. The organization is also known as: Agilent Technologies, Inc..


Papers
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Journal ArticleDOI
15 Sep 2012-Talanta
TL;DR: Method detection levels (MDLs) were determined to describe analyte concentrations sufficient to provide a signal with 99% certainty of detection and method validation confirmed satisfactory method stability over intra-day and inter-day analyses of tap water and tertiary treated effluent samples.

83 citations

Patent
24 Aug 2001
TL;DR: In this paper, a method and system for calibrating a test system and a vector network analyzer using models of unknown calibration standards for a standards-based calibration is presented, where the models are selected for the unknown standards and performance data for each different calibration standard of the set are measured by the test system.
Abstract: A method and system for calibrating a test system and a vector network analyzer use models of unknown calibration standards for a standards-based calibration. The models are selected for the unknown standards and performance data for each different calibration standard of the set are measured by the test system. The performance data is used to optimize the models by adjusting element values of constituent elements of the models. The optimized models are used to calibrate the test system such that the test system corrects for any imperfections in performance measurements taken of a device under test measured by the calibrated test system. The present invention further can correct for affects of a test fixture in device under test measurements. The method can also determine calibration coefficients for unknown calibration standards by extracting the calibration coefficients from the optimized models.

83 citations

Journal ArticleDOI
TL;DR: Work is underway at NIST and among members of the gene expression array community to further define two RNA reference materials that would help in standardization of gene-expression technologies and make them available.
Abstract: A workshop entitled "Metrology and Standards Needs for Gene Expression Technologies: Universal RNA Standards" was held in March 2003 to define the requirements for standardizing RNA-based molecular assays, specifically microarray and quantitative reverse-transcriptase-PCR technologies. NIST sponsored the workshop, and participants represented government, industry, academia, and clinic. Workshop participants concluded that as a first step, two RNA reference materials could be defined that would help in standardization of gene-expression technologies: an Assay Process Reference Material, and a Universal Array Hybridization Reference Material. The specific characteristics of these two standardized materials were broadly outlined. The Assay Process Material was proposed to be a pool of 96 expressed human sequences of defined composition, cloned in a defined vector and pooled in prescribed ways. The Universal Array Hybridization Material was defined as a pool of 12 "alien" synthetic sequences not expressed in any known genome to be used to control for variability in array hybridization methods. Work is underway at NIST and among members of the gene expression array community to further define these materials and make them available.

82 citations

Journal ArticleDOI
TL;DR: Accepted methods for testing integrated circuits, such as the fault models examined here, require ongoing research and continual adaptation to accommodate increasing circuit size, growing defect subtlety, and less varied manufacturing processes.
Abstract: Accepted methods for testing integrated circuits, such as the fault models examined here, require ongoing research and continual adaptation to accommodate increasing circuit size, growing defect subtlety, and less varied manufacturing processes.

82 citations

Patent
15 Mar 2000
TL;DR: In this paper, a multi-stage evacuation procedure is combined with the slower deposition rate of silicon dioxide, and the resulting mask has a surprisingly low defect density when compared to the conventional high-energy radiation mask.
Abstract: A method of fabricating a high energy radiation mask, such as a laser ablation mask for manufacturing inkjet printheads, includes a multi-stage evacuation process and/or a step of reducing the deposition rate of silicon dioxide during formation of a dielectric stack. When the multi-stage evacuation procedure is combined with the slower deposition rate of silicon dioxide, the resulting mask has a surprisingly low defect density. In the first embodiment, the evacuation procedure is initiated using a low-rate first evacuation connection. The relatively slow purging of a vacuum chamber in which the dielectric stack is subsequently formed controls turbulence and environmental changes that can generate contamination and water along the surface of the substrate on which the dielectric stack is formed. When a pressure setpoint is reached, a second roughing connection is activated to increase the speed of the procedure. The second connection has a higher maximum rate than the first connection. In the preferred embodiment, there is an overlap in the activations of the first and second connections. When another setpoint is reached, a high vacuum connection is activated in order to bring the vacuum chamber to a high vacuum condition for deposition of the dielectric stack. The dielectric stack includes alternating layers of higher refractive index material and low refractive index material. The low refractive index material is silicon dioxide that is deposited at a rate in the optimal range of 1.0 Å/second to 3.0 Å/second. Practical considerations dictate a range of 1.6 Å/second to 2.4 Å/second.

82 citations


Authors

Showing all 7402 results

NameH-indexPapersCitations
Hongjie Dai197570182579
Zhuang Liu14953587662
Jie Liu131153168891
Thomas Quertermous10340552437
John E. Bowers102176749290
Roy G. Gordon8944931058
Masaru Tomita7667740415
Stuart Lindsay7434722224
Ron Shamir7431923670
W. Richard McCombie7114464155
Tomoyoshi Soga7139221209
Michael R. Krames6532118448
Shabaz Mohammed6418817254
Geert Leus6260919492
Giuseppe Gigli6154115159
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20231
20228
2021142
2020157
2019168
2018164