Exploring high-dimensional data space: Identifying optimal process conditions in photovoltaics

Suh, Changwon; Biagioni, David; Glynn, Stephen; Scharf, John; Contreras, Miguel Á.; Noufi, R.; Jones, Wesley B.

doi:10.1109/pvsc.2011.6186065

Cited by 4 publications

(2 citation statements)

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“…There are still challenges in fully vetting the multi‐way framework. These include, for example, identifying the proper choice of preprocessing, incorporation of categorical data, and accommodation of nonlinear relationships, which are partly resolved by properly adapting advanced algorithms (for example, see ). Nevertheless, the multi‐way approach addresses some key challenges in understanding cell degradation, namely, the integration and analysis of heterogeneous and multi‐scale data for improved process control and design.…”

Section: Discussionmentioning

confidence: 99%

Analysis of governing factors for photovoltaic loss mechanism of n-CdS/p-CdTe heterojunction via multi-way data decomposition

Biagioni

Graham

Albin

et al. 2013

Prog. Photovolt: Res. Appl.

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An analytical framework for identifying key factors of the degradation of photovoltaic efficiency over time is presented. We demonstrate that, in many photovoltaic experimental settings, reliability data sets are easily cast in a multi‐ or N‐way format. We adopt a statistical technique, N‐way partial least squares, that generates a multi‐linear model using all of the data simultaneously. With this approach, we are able to model variables of interest such as cell efficiency while representing the data in a lower‐dimensional space in which salient features are more easily identified. We illustrate our approach with reliability data for CdS/CdTe heterojunction solar cell devices. Even with the inclusion of a noisy parameter such as the net acceptor density, and with a relatively small number of devices, we automatically identify key factors that are highly related to performance degradation. In particular, the conductance at the back contact is related to short stress‐time degradation (0–300 h), whereas the net acceptor density near the junction (at +0.08 V DC bias) is correlated with more gradual, long stress‐time degradation (300–1000 h). These notable degradation modes are explained with respect to our processing conditions and Cu‐diffusion in the cells. Copyright © 2013 John Wiley & Sons, Ltd.

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Section: Discussionmentioning

confidence: 99%

Analysis of governing factors for photovoltaic loss mechanism of n-CdS/p-CdTe heterojunction via multi-way data decomposition

Biagioni

Graham

Albin

et al. 2013

Prog. Photovolt: Res. Appl.

Self Cite

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“…In this study, we use an alternative approach to choosing the model descriptors for studying the absorber layers in photovoltaic cells, which has been suggested elsewhere in studying transparent conductive oxides, 19 instead of using structural descriptors we use process descriptors. Process descriptors illustrate the fabrication process of the material in a data set.…”

Section: ■ Introductionmentioning

confidence: 99%

Process-Function Data Mining for the Discovery of Solid-State Iron-Oxide PV

et al. 2017

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Data mining tools have been known to be useful for analyzing large material data sets generated by high-throughput methods. Typically, the descriptors used for the analysis are structural descriptors, which can be difficult to obtain and to tune according to the results of the analysis. In this Research Article, we show the use of deposition process parameters as descriptors for analysis of a photovoltaics data set. To create a data set, solar cell libraries were fabricated using iron oxide as the absorber layer deposited using different deposition parameters, and the photovoltaic performance was measured. The data was then used to build models using genetic programing and stepwise regression. These models showed which deposition parameters should be used to get photovoltaic cells with higher performance. The iron oxide library fabricated based on the model predictions showed a higher performance than any of the previous libraries, which demonstrates that deposition process parameters can be used to model photovoltaic performance and lead to higher performing cells. This is a promising technique toward using data mining tools for discovery and fabrication of high performance photovoltaic materials.

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Finding Relevant Parameters for the Thin‐film Photovoltaic Cells Production Process with the Application of Data Mining Methods

Ulaczyk

Morawiec

Zabierowski

et al. 2017

Molecular Informatics

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A data mining approach is proposed as a useful tool for the control parameters analysis of the 3-stage CIGSe photovoltaic cell production process, in order to find variables that are the most relevant for cell electric parameters and efficiency. The analysed data set consists of stage duration times, heater power values as well as temperatures for the element sources and the substrate - there are 14 variables per sample in total. The most relevant variables of the process have been found based on the so-called random forest analysis with the application of the Boruta algorithm. 118 CIGSe samples, prepared at Institut des Matériaux Jean Rouxel, were analysed. The results are close to experimental knowledge on the CIGSe cells production process. They bring new evidence to production parameters of new cells and further research.

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Exploring high-dimensional data space: Identifying optimal process conditions in photovoltaics

Cited by 4 publications

References 20 publications

Analysis of governing factors for photovoltaic loss mechanism of n-CdS/p-CdTe heterojunction via multi-way data decomposition

Analysis of governing factors for photovoltaic loss mechanism of n-CdS/p-CdTe heterojunction via multi-way data decomposition

Process-Function Data Mining for the Discovery of Solid-State Iron-Oxide PV

Finding Relevant Parameters for the Thin‐film Photovoltaic Cells Production Process with the Application of Data Mining Methods

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