Hydrogen-driven surface amorphization of the transparent oxide semiconductor thin-films for photovoltaic applications

Kim, Seohan; Yoon, Jung‐Sik; Park, Sung Min; Bang, Joonho; Song, Pung Keun

doi:10.1016/j.apsusc.2021.149702

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…The summation layer is the last [38,39]. The ANFIS layers outputs were computed using equation [1][2][3][4][5][6].…”

Section: Adaptive Neuro-fuzzy Inference Systems (Anfis)mentioning

confidence: 99%

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Modeling the electrical properties of heterojunctions using ANFIS, ANFIS-GA and ANFIS-PSO Models

Mohamed

2023

Phys. Scr.

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The present research introduces a theoretical study that aims to utilize ANFIS in estimating and predicting the electrical behavior of heterojunctions. For this purpose, five different heterojunctions were chosen. The experimental datasets that represent the electrical behavior of the chosen heterojunctions were extracted and employed in ANFIS as targets. To enhance the ANFIS performance two hybrid heuristic algorithms, genetic algorithm (GAs) and particle swarm optimization (PSO) were combined with ANFIS. The major contribution of the current research is to predict the electric characteristics of heterojunctions using ANFIS and increase the modeling accuracy of ANFIS by optimizing the premise and consequent parameters using (GAs) and (PSO). Also, compare the proportion of enhancement produced by using ANFIS-GA and ANFIS-PSO to decide which of them is more powerful under the study conditions. However, to the author’s knowledge, the presented goals have not been investigated before for heterojunctions. The mean squared error (MSE), the correlation coefficient (R2), and the standard deviation error (Std. error) were calculated for all trained models. The modeling errors of ANFIS-GA and ANFIS-PSO were compared to the error values produced by ANFIS. According to modeling results, simulation ANFIS outputs follow the experimental data patterns in excellent response. Predictions of electrical characteristics for heterojunctions using the trained models provide acceptable results where the MSE values obtained by training ANFIS-PSO are lower than their values obtained by ANFIS and ANFIS-GA models. The improvements in average percentages in ANFIS performance when combined with GA and PSO are equal to 2.2% and 3%, respectively. Consequently, the proposed ANFIS-PSO model is more accurate in predicting the electrical behavior of heterojunctions under the study conditions.

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“…The summation layer is the last [38,39]. The ANFIS layers outputs were computed using equation [1][2][3][4][5][6].…”

Section: Adaptive Neuro-fuzzy Inference Systems (Anfis)mentioning

confidence: 99%

“…The heterojunctions attract attention because of their great potential in numerous applications [1][2][3][4]. The physical properties of heterojunctions have been a field of intensive study for many years, due to their interesting electrical and optical properties.…”

Section: Introductionmentioning

confidence: 99%

Modeling the electrical properties of heterojunctions using ANFIS, ANFIS-GA and ANFIS-PSO Models

Mohamed

2023

Phys. Scr.

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“…Indium tin oxide (ITO) has been intensively studied in the past, mainly as a TCO material [26], but also regarding its TE properties [27,28]. Despite the outstanding optical and electrical properties of ITO, it is difficult to obtain ITO-based films that exhibit adequate electronic, optical and thermal properties simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…We have previously reported that an ITO thin film with an amorphous structure can exhibit fairly high σ without an accompanying increase of n and still have low κ l because of high carrier mobility due to non-directional In 5 s bonding with oxygen [27,28]. However, amorphous thin films have relatively poor electrical conductivity compared to crystalline oxide TE materials, which is an inherent limitation of amorphous metal oxides [26,34,35].…”

Section: Introductionmentioning

confidence: 99%

Embedded Nanopattern for Selectively Suppressed Thermal Conductivity and Enhanced Transparency in a Transparent Conducting Oxide Film

et al. 2022

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Dual functional high entropy perovskite La(Mn0.2Cr0.2Fe0.2Co0.2Ni0.2)O3 interfacial transition layer modified Cu2Y2O5/CdIn2O4 transparent pn junction towards photovoltaic enhancement

Wang,

He,

Wang

et al. 2024

Ceramics International

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Hydrogen-driven surface amorphization of the transparent oxide semiconductor thin-films for photovoltaic applications

Cited by 5 publications

References 31 publications

Modeling the electrical properties of heterojunctions using ANFIS, ANFIS-GA and ANFIS-PSO Models

Modeling the electrical properties of heterojunctions using ANFIS, ANFIS-GA and ANFIS-PSO Models

Embedded Nanopattern for Selectively Suppressed Thermal Conductivity and Enhanced Transparency in a Transparent Conducting Oxide Film

Dual functional high entropy perovskite La(Mn0.2Cr0.2Fe0.2Co0.2Ni0.2)O3 interfacial transition layer modified Cu2Y2O5/CdIn2O4 transparent pn junction towards photovoltaic enhancement

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