Organic RFID Tags

Myny, Kris; Vicca, Peter; Monique, J.; Aerle, N. A. J. M. van; Gerwin, H.; Genoe, Jan; Dehaene, Wim; Heremans, Paul

doi:10.5772/7985

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…The current work is motivated by the need to analyze and understand big datasets arising in the manufacturing of organic electronics (OEs). OE is a new sustainable class of device, spanning organic transistors [26,27], organic solar cells [28,29], diode lighting [30,31], flexible displays [32], integrated smart systems such as RFIDs (Radio-frequency Identification) [33,34], smart textiles [35], artificial skin [36], and implantable medical devices and sensors [37,38]. The critical and highly desirable features of OEs are their cost, and their rapid and low-temperature roll-to-roll fabrication.…”

Section: Applicationmentioning

confidence: 99%

Learning Manifolds from Dynamic Process Data

et al. 2020

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Scientific data, generated by computational models or from experiments, are typically results of nonlinear interactions among several latent processes. Such datasets are typically high-dimensional and exhibit strong temporal correlations. Better understanding of the underlying processes requires mapping such data to a low-dimensional manifold where the dynamics of the latent processes are evident. While nonlinear spectral dimensionality reduction methods, e.g., Isomap, and their scalable variants, are conceptually fit candidates for obtaining such a mapping, the presence of the strong temporal correlation in the data can significantly impact these methods. In this paper, we first show why such methods fail when dealing with dynamic process data. A novel method, Entropy-Isomap, is proposed to handle this shortcoming. We demonstrate the effectiveness of the proposed method in the context of understanding the fabrication process of organic materials. The resulting low-dimensional representation correctly characterizes the process control variables and allows for informative visualization of the material morphology evolution.

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Section: Applicationmentioning

confidence: 99%

Learning Manifolds from Dynamic Process Data

et al. 2020

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“…The current work is motivated by the need to analyze and understand big data sets arising in the manufacturing of Organic Electronics (OE). OE is a new sustainable class of devices, spanning organic transistors [17], [18], organic solar cells [19], [20], diode lighting [21], [22], flexible displays [23], integrated smart systems such as RFIDs [24], [25], smart textiles [26], artificial skin [27], and implantable medical devices and sensors [28], [29]. The critical and highly desired feature of OE is inexpensive, rapid and low-temperature roll-to-roll fabrication.…”

Section: Applicationmentioning

confidence: 99%

Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes

Schoeneman

Chandola

Napp

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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Scientific and engineering processes deliver massive high-dimensional data sets that are generated as non-linear transformations of an initial state and few process parameters. Mapping such data to a low-dimensional manifold facilitates better understanding of the underlying processes, and enables their optimization. In this paper, we first show that off-theshelf non-linear spectral dimensionality reduction methods, e.g., Isomap, fail for such data, primarily due to the presence of strong temporal correlations. Then, we propose a novel method, Entropy-Isomap, to address the issue. The proposed method is successfully applied to large data describing a fabrication process of organic materials. The resulting lowdimensional representation correctly captures process control variables, allows for low-dimensional visualization of the material morphology evolution, and provides key insights to improve the process.

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“…The ability to deposit organic semiconductors as thin films or nanostructures on a range of low cost, including flexible, substrates has led to applications ranging from gas sensors and smart tags to light emitting diodes and solar cells. [1][2][3][4][5][6] Despite those successes there are still many challenges, particularly given the need to integrate the organic devices with transistors, providing logic, data storage and drivers, which today are almost inevitably inorganic. Organic transistors have thus far not succeeded because they have yet to simultaneously achieve stability, reproducibility and scalability of manufacture.…”

Section: Introductionmentioning

confidence: 99%

Self-Assembled Molecular Nanowires for High-Performance Organic Transistors

Fleet¹,

Stott

Villis

et al. 2017

ACS Appl. Mater. Interfaces

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While organic semiconductors provide tantalizing possibilities for low-cost, light-weight, flexible electronic devices, their current use in transistors-the fundamental building block-is rather limited as their speed and reliability are not competitive with those of their inorganic counterparts and are simply too poor for many practical applications. Through self-assembly, highly ordered nanostructures can be prepared that have more competitive transport characteristics; however, no simple, scalable method has been discovered that can produce devices on the basis of such nanostructures. Here, we show how transistors of self-assembled molecular nanowires can be fabricated using a scalable, gradient sublimation technique, which have dramatically improved characteristics compared to those of their thin-film counterparts, both in terms of performance and stability. Nanowire devices based on copper phthalocyanine have been fabricated with threshold voltages as low as -2.1 V, high on/off ratios of 10, small subthreshold swings of 0.9 V/decade, and mobilities of 0.6 cm/V s, and lower trap energies as deduced from temperature-dependent properties, in line with leading organic semiconductors involving more complex fabrication. High-performance transistors manufactured using our scalable deposition technique, compatible with flexible substrates, could enable integrated all-organic chips implementing conventional as well as neuromorphic computation and combining sensors, logic, data storage, drivers, and displays.

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Organic RFID Tags

Cited by 5 publications

References 6 publications

Learning Manifolds from Dynamic Process Data

Learning Manifolds from Dynamic Process Data

Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes

Self-Assembled Molecular Nanowires for High-Performance Organic Transistors

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