The Target Material Influence on the Current Pulse during High Power Pulsed Magnetron Sputtering

Moens, Filip; Konstantinidis, Stéphanos; Depla, Diederik

doi:10.3389/fphy.2017.00051

Cited by 11 publications

(6 citation statements)

References 46 publications

(52 reference statements)

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“…This is also reported by [36] that the ion-toatom ratio near the substrate increased around 3 times when the peak current changed from 113 to 185 A. The current waveforms for different target materials showed that the peak current value and the current plateau are corresponding with the rarefaction (or sputter wind) and ionization rate of metals, respectively [37]. The comparison of the current waveforms of HiPIMS and 1.0 A-DC-superimposed HiPIMS is indicative of the higher peak current in HiPIMS while the slope of the current exceeds for DC-HiPIMS [38].…”

Section: Introductionsupporting

confidence: 71%

The experimental approach into the influence of external inductance on the discharge characteristic of HiPIMS

et al. 2019

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The main objective of the current paper is to describe the effect of external inductance (EI) on the current discharge waveforms of HiPIMS at different pulse-on time (P on) and its relation with static deposition rate and topographical properties of deposited titanium thin films, which is investigated by scanning electron microscope and atomic force microscope. It has shown that the higher the EI, independent of the P on , the higher the peak power is. The delay time also extensively increases when an EI is implemented into the circuit. However, the rise time does not have a linear dependency with the EI and its behavior changes to some extent at different P on. By increasing the EI from zero to 30 mH at P on = 60 μs, the peak power subsequently rises from 11 to 32 kW at constant time-average power. Meanwhile, the deposition rate decreases from 8.5 to 1.5 nm/min, which is mainly attributed to the metal ions return to the target surface and nonlinear dependency of sputtering yield with applied voltage. It was also revealed that the higher peak power has no special effect on the surface roughness of titanium thin films deposited by HiPIMS.

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Section: Introductionsupporting

confidence: 71%

The experimental approach into the influence of external inductance on the discharge characteristic of HiPIMS

et al. 2019

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“…the gas in front of the target surface is more ionized or dissociated, which may happen at elevated discharge current densities. The latter might play an important role in HiPIMS [18,19].…”

Section: Discussionmentioning

confidence: 99%

Influence of chemisorption on the double hysteresis phenomenon during reactive sputtering

Bever

Strijckmans

Depla

2023

Applied Surface Science

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“…Genomics and proteomics on emergent properties, clonal fate, protein types [6]- [8]. Chemistry, physics, and material science on laser devices, ion energy, and photoelectrochemical power [9]- [11]. Environmental and earth science on climate and ocean dynamics [12]- [14].…”

Section: A Background Of Variable Dependence Detectionmentioning

confidence: 99%

“…Equ. (9) shows the parameter update rule. The partial derivative of the first term J 1st term j (W) of the objective function is derived by the chain rule, where z denotes the outputs of all the neurons.…”

Section: B Formulation and Definitionmentioning

confidence: 99%

NNR-GL: A Measure to Detect Co-Nonlinearity Based on Neural Network Regression Regularized by Group Lasso

et al. 2021

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For finding keys to understand and elucidate a phenomenon, it is essential to detect dependences among variables, and so measures for that have been proposed. Correlation coefficient and its variants are most common, but they only detect a linear dependence (co-linearity) between two variables. Some recent measures can detect a nonlinear dependence (co-nonlinearity) by means of kernelization or segmentation. They are supposed to handle two variables only and open to discussion with regard to performance in detection and difficulty in setup. There is room for a novel measure based on Neural Networks (NNs), since usual NNs aim at prediction but not at variable dependence detection. For the high-performance detection of co-nonlinearities among multi variables, we propose a measure called NNR-GL based on Neural Network Regression (NNR) regularized by Group Lasso (GL). NNR-GL embodies the detection through multi-input single-output regression by NNR and regularization on the input layer by GL. NNR-GL then calculates how strong the detected co-nonlinearities are by unifying the regression performance and the weights on input variables. We conducted experiments using artificial data to examine the behaviors and fundamental effectiveness of NNR-GL. The performance was estimated by a comprehensive detection performance criterion (CDP-AUC in short), which is the mean of area under curves representing true positive and true negative detections. NNR-GL achieved the values of CDP-AUC from 0.7472 to 0.9681, where 0 means complete failure and 1 means complete success in detection. These values were consistently higher than those from 0.5972 to 0.9259 of the conventional measures for all the different conditions of dependence, data size, and noise rate. Consequently, the effectiveness and robustness of NNR-GL were clearly confirmed.

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The Target Material Influence on the Current Pulse during High Power Pulsed Magnetron Sputtering

Cited by 11 publications

References 46 publications

The experimental approach into the influence of external inductance on the discharge characteristic of HiPIMS

The experimental approach into the influence of external inductance on the discharge characteristic of HiPIMS

Influence of chemisorption on the double hysteresis phenomenon during reactive sputtering

NNR-GL: A Measure to Detect Co-Nonlinearity Based on Neural Network Regression Regularized by Group Lasso

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