Large-scale data analysis of PECVD amorphous silicon interface passivation layer via the optical emission spectra for parameterized PCA

Huang, Ho‐Chuan; Kau, Li Han; Wang, Ho Song; Hsieh, Yu Lin; Lee, Chien Chieh; Fuh, Yiin Kuen; Li, Tomi T.

doi:10.1007/s00170-018-2938-1

Cited by 9 publications

(4 citation statements)

References 39 publications

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“…3. Results and discussion: The overall methodology was implemented in a three-phase process [25], as schematically presented in Fig. 1, which includes the first phase of data pre-processing and the second phase of performance modelling via a suitable algorithm and the third phase is the film characterisation and validation.…”

Section: Methodsmentioning

confidence: 99%

“…Outlier elimination can be implemented in the pre-processing via the three steps: (i) irreverent radicals evaluation and elimination as recorded in-situ during PECVD deposition and selecting segments of relevant radicals that satisfy phase transition of amorphous to nanocrystalline in the PECVD process (Hα, Hβ etc. ); (ii) establishing OES data-based SCADA (Supervisory Control and Data Acquisition); and (iii) film analysis and property validation [25]. A large-scale data analysis will implement the PCA and three aspects should be performed as follows: (i) participating radicals (Hα, Hβ, Si*, SiH*) will be intentionally selected as the main control scheme; (ii) contributing radical (such as Hα) was illustrated as regime of interest; and (iii) outlier elimination.…”

Section: Methodsmentioning

confidence: 99%

“…Subsequently, the concept of the principal component analysis (PCA) in large‐scale analysis [21–26] was used to correlate OES data and the resultant nc‐Si:H film structures properties such as crystallisation rate (88.6%) and crystallised size (2.56 nm). In addition, the health value [25–28] was calculated and associated with the nc‐Si:H film measurements.…”

Section: Introductionmentioning

confidence: 99%

“…To quantify the deviation calculated from the big data of OES spectra, the transition to nanocrystalline silicon (nc-Si:H) can be assessed. Subsequently, the concept of the principal component analysis (PCA) in large-scale analysis [21][22][23][24][25][26] was used to correlate OES data and the resultant nc-Si:H film structures properties such as crystallisation rate (88.6%) and crystallised size (2.56 nm). In addition, the health value [25][26][27][28] was calculated and associated with the nc-Si:H film measurements.…”

mentioning

confidence: 99%

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Large‐scale data principal component analysis of phase transition from a‐Si:H to nc‐Si:H using PECVD optical emission spectra

Kau

Huang

Chang

et al. 2020

Micro & Nano Letters

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Phase transformation of hydrogenated nanocrystalline silicon (nc-Si:H) films was essential in the solar cells industry. Therefore, the plasma chemistry monitoring of in-situ diagnostics optical emission spectroscopy (OES) of plasma-enhanced chemical vapour deposition (PECVD) is deemed crucial for investigating the phase transformation. The proposed PC2-OES algorithm can be used to monitor the PECVD process health condition of the crystallisation rate at different hydrogen dilution ratios. Principal component analysis (PCA) was performed to distinguish the crucial role of nc-Si:H emission characteristics of plasma chemistry and the resultant crystallisation rate. Measurement results revealed that OES spectra characterisation confirmed that the crystallisation rate index (Hα*/SiH*) was highly correlated to hydrogen dilution ratio (R) and phase transformation of nc-Si:H film. The proposed PCA-based evaluation method will provide valuable information to reflect consistently the crystallisation rate of deposited films with different hydrogen dilution ratio and possibly other processing parameters of mass flow rate and applied power density.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Large‐scale data principal component analysis of phase transition from a‐Si:H to nc‐Si:H using PECVD optical emission spectra

Kau

Huang

Chang

et al. 2020

Micro & Nano Letters

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Self‐Adaptive Plasma Chemistry and Intelligent Plasma Medicine

Lin

Yan

Lee

2021

Advanced Intelligent Systems

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Plasma‐based biomedical applications rely on the reactive oxygen and nitrogen species generated in cold atmospheric plasmas, where complex chemical kinetic schemes occur. The optimization of plasma medicine is thus required for each specific biomedical purpose. In the view of pharmacology, it is to optimize the active pharmaceutical ingredients. This work is thus the first attempt of such a complex task utilizing the recent development of machine learning technologies. Herein, a general method of passive plasma chemical diagnostics and optimization in real time is proposed. Based on spontaneous emission spectroscopy, an artificial neural network provides the gas chemical compositions along with other information such as temperatures. The information further passes through the second neural network which outputs the adjustments of external control inputs including energy, gas injections, and extractions to optimize the plasma chemistry.

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Optimization of Silicon Heterojunction Interface Passivation on p‐ and n‐Type Wafers Using Optical Emission Spectroscopy

Özkol

Wagner

Ruske

et al. 2022

Physica Status Solidi (a)

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To increase the efficiency in p‐type wafer‐based silicon heterojunction (SHJ) technology, one of the most crucial challenges is the achievement of excellent surface passivation. Herein, chemical passivation techniques known for n‐type technology are successfully applied on p‐type float–zone (FZ) wafers, and wafer surface passivation quality is correlated with parameters from plasma diagnostics, namely crystallization rate and electron temperature indices. It is shown that plasma ignition at higher powers than deposition powers enhances effective minority carrier lifetimes τeff fourfold for p‐ (0.6–2.1 ms) and sixfold for n‐type (0.6–3.2 ms) wafers while giving opportunity to process under lower electron temperature indices during the nucleation phase. A subsequent hydrogen plasma treatment has a further beneficial effect on chemical passivation, leading to high effective minority carrier lifetimes of 4.5 and 3.1 ms, and implies open‐circuit voltages, i‐VOC, of 735 and 720 mV for p‐ and n‐type wafers, respectively. In particular, cell precursors built on p‐type wafers demonstrate excellent surface passivation with τeff and i‐VOC (4.1 ms and 745 mV). Using these process optimizations, SHJ cells on both p‐ and n‐type wafers are fabricated with efficiencies exceeding 21%.

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Large-scale data analysis of PECVD amorphous silicon interface passivation layer via the optical emission spectra for parameterized PCA

Cited by 9 publications

References 39 publications

Large‐scale data principal component analysis of phase transition from a‐Si:H to nc‐Si:H using PECVD optical emission spectra

Large‐scale data principal component analysis of phase transition from a‐Si:H to nc‐Si:H using PECVD optical emission spectra

Self‐Adaptive Plasma Chemistry and Intelligent Plasma Medicine

Optimization of Silicon Heterojunction Interface Passivation on p‐ and n‐Type Wafers Using Optical Emission Spectroscopy

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