Exploring high-dimensional data space : identifying optimal process conditions in photovoltaics : preprint / Changwon Suh [and others].

Format:

Book

Conference/Event

Government document

Contributor:

Suh, Changwon.

National Renewable Energy Laboratory (U.S.)

University of Colorado Boulder

Conference Name:

IEEE Photovoltaic Specialists Conference (37th : 2011 : Seattle, Wash.)

Series:

Conference paper (National Renewable Energy Laboratory (U.S.)) ; NREL/CP-2 C 00-50693.

NREL/CP ; 2C00-50693

Language:

English

Subjects (All):

Thin films--Research.

Thin films.

Data mining.

Photovoltaic power systems--Data processing.

Photovoltaic power systems.

Visualization.

Data Mining.

Medical Subjects:

Data Mining.

Genre:

technical reports.

Technical reports

Technical reports.

Physical Description:

1 online resource (6 pages) : color illustrations.

Place of Publication:

[Golden, CO] : National Renewable Energy Laboratory, [2011]

Summary:

We demonstrate how advanced exploratory data analysis coupled to data-mining techniques can be used to scrutinize the high-dimensional data space of photovoltaics in the context of thin films of Al-doped ZnO (AZO), which are essential materials as a transparent conducting oxide (TCO) layer in CuInxGa1-xSe2 (CIGS) solar cells. AZO data space, wherein each sample is synthesized from a different process history and assessed with various characterizations, is transformed, reorganized, and visualized in order to extract optimal process conditions. The data-analysis methods used include parallel coordinates, diffusion maps, and hierarchical agglomerative clustering algorithms combined with diffusion map embedding.

Notes:

Title from title screen (viewed August 1, 2011).

"July 2011."

"Presented at the 37th IEEE Photovoltaic Specialists Conference (PVSC 37), Seattle, Washington, June 19-24, 2011."

Includes bibliographical references (page 6).

OCLC:

743793317