Nanoscale thin film transfer using elastomer-covered roll with buffer cavities

Kim, Jung Yup; Choi, Hyun-Ju; Woo, Chang Su

doi:10.1007/s12541-014-0391-0

Cited by 10 publications

(2 citation statements)

References 8 publications

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“…A variety of mass-transfer techniques have been proposed to assemble Micro-LEDs, such as laser-Assisted mass transfer [14]- [17], fluidic self-assembly [18], [19], electrostatic mass transfer [20]- [22], micro vacuumbased transfer [8], elastomeric stamp or roll-based mass transfer [23], [24] and the adhesive stamp mass transfer adopted in this work [25]- [28]. Laser-assisted mass transfer is developed for selective micro-LEDs printing upon laser irradiation on the light-reactive layers [14]- [17].…”

Section: Introductionmentioning

confidence: 99%

“…Individual vacuum pressure control of each device enables large-scale and selective Micro-LED transfer without any adhesive and heating/compressing process. Roll-based mass transfer which developed from elastomer stamp transfer enables a largescale, high yield and rapid transfer process for micro-LEDs display [23], [24]. However, the cost of device is high and the process is complex.…”

Section: Introductionmentioning

confidence: 99%

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Optimization on Adhesive Stamp Mass-Transfer of Micro-LEDs With Support Vector Machine Model

Guo

et al. 2020

IEEE J. Electron Devices Soc.

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In this work, the process of adhesive stamp mass-transfer of micro light-emitting diode (micro-LED) is optimized by a Support Vector Machine (SVM) model. The pickup experiments have been performed repeatedly for hundreds of times from which the separation speed and the force between the stamp and the donor substrate are extracted as signal features. The SVM model with a Gaussian kernel function is designed to classify pickup results into success and failure. In addition, the optimal cost parameter C as well as the Gaussian kernel function parameter gamma (γ) has been optimized, leading to the improvement of the classification by Particle Swarm Optimization (PSO) algorithm. Finally, an 85% classification accuracy is achieved based on the SVM model, implying that more sophisticated definition of signal features is demanded in future work. INDEX TERMS Adhesive stamp, mass-transfer, micro-LEDs, support vector machine model, particle swarm optimization.

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