Linda Jäckel, Andreas Zienert, Annekathrin Zeun, Anna-Sophie Seidel, Jörg Schuster
We investigate Si epitaxy using 3D reactor scale computational fluid dynamics simulations coupled with surface chemistry models for the growth of pure silicon and phosphorus-doped silicon (Si:P) films. We focus on low temperature Si and Si:P processes using dichlorosilane (DCS) and phosphine. Based on existing DCS-based Si chemistry models for higher process temperatures, we developed a new kinetic chemistry model for low temperature Si epitaxy. To include doping, we developed an additional empirical model for Si:P epitaxy as there is not sufficient qualitative data on phosphine chemistry available for a kinetic chemistry model. This work provides Si and Si:P surface chemistry models, which allow reactor scale process simulations to get valuable process insights, enabling rational process optimization and supporting process transfer. Process optimization is demonstrated through process parameter variation with the main goal being the reduction of Si process variability by increasing within-wafer growth rate homogeneity.
{"title":"Surface chemistry models for low temperature Si epitaxy process simulation in a single-wafer reactor","authors":"Linda Jäckel, Andreas Zienert, Annekathrin Zeun, Anna-Sophie Seidel, Jörg Schuster","doi":"10.1116/6.0003340","DOIUrl":"https://doi.org/10.1116/6.0003340","url":null,"abstract":"We investigate Si epitaxy using 3D reactor scale computational fluid dynamics simulations coupled with surface chemistry models for the growth of pure silicon and phosphorus-doped silicon (Si:P) films. We focus on low temperature Si and Si:P processes using dichlorosilane (DCS) and phosphine. Based on existing DCS-based Si chemistry models for higher process temperatures, we developed a new kinetic chemistry model for low temperature Si epitaxy. To include doping, we developed an additional empirical model for Si:P epitaxy as there is not sufficient qualitative data on phosphine chemistry available for a kinetic chemistry model. This work provides Si and Si:P surface chemistry models, which allow reactor scale process simulations to get valuable process insights, enabling rational process optimization and supporting process transfer. Process optimization is demonstrated through process parameter variation with the main goal being the reduction of Si process variability by increasing within-wafer growth rate homogeneity.","PeriodicalId":17490,"journal":{"name":"Journal of Vacuum Science & Technology A","volume":"30 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140056291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Joseph R. Vella, Qinzhen Hao, Vincent M. Donnelly, David B. Graves
Atomic layer etching is intrinsically dynamic as it involves sequential and repeated exposures of a surface to be etched with different species at different energies. The composition and structure of the near surface region change in both time and depth. Full understanding of this process requires resolving both temporal and spatial variations. In this work, we consider silicon (Si) atomic layer etching (ALE) by alternating exposure to chlorine gas (Cl2) and argon ions (Ar+). Molecular dynamics (MD) simulations are compared to experimental measurements with the aim of better understanding the dynamics of ALE and to test the simulation procedure. The simulations help to more fully interpret the experimental measurements. Optical emission measured just above the surface being etched can be related to etch products and can, therefore, be directly compared to simulation predictions. The simulations capture the measured initial product distribution leaving the surface and match the measured etch per cycle reasonably well. While simulations demonstrate the importance of ion-induced surface damage and mixing into a layer below the surface, the depth of which depends mainly on ion energy, the experiments suggest there is more Cl mixed into the layer than the MD procedure predicts.
{"title":"Dynamics of plasma atomic layer etching: Molecular dynamics simulations and optical emission spectroscopy","authors":"Joseph R. Vella, Qinzhen Hao, Vincent M. Donnelly, David B. Graves","doi":"10.1116/6.0003011","DOIUrl":"https://doi.org/10.1116/6.0003011","url":null,"abstract":"Atomic layer etching is intrinsically dynamic as it involves sequential and repeated exposures of a surface to be etched with different species at different energies. The composition and structure of the near surface region change in both time and depth. Full understanding of this process requires resolving both temporal and spatial variations. In this work, we consider silicon (Si) atomic layer etching (ALE) by alternating exposure to chlorine gas (Cl2) and argon ions (Ar+). Molecular dynamics (MD) simulations are compared to experimental measurements with the aim of better understanding the dynamics of ALE and to test the simulation procedure. The simulations help to more fully interpret the experimental measurements. Optical emission measured just above the surface being etched can be related to etch products and can, therefore, be directly compared to simulation predictions. The simulations capture the measured initial product distribution leaving the surface and match the measured etch per cycle reasonably well. While simulations demonstrate the importance of ion-induced surface damage and mixing into a layer below the surface, the depth of which depends mainly on ion energy, the experiments suggest there is more Cl mixed into the layer than the MD procedure predicts.","PeriodicalId":17490,"journal":{"name":"Journal of Vacuum Science & Technology A","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136346621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adam Pranda, Steven Grzeskowiak, Yu- Hao Tsai, Yusuke Yoshida, Eric Liu, Yun Han, Peter Biolsi, Ken Kobayashi, Nobuyuki Ikezawa
Low-k materials are an integral component in the advancement of semiconductor device performance by reducing parasitic capacitance and enabling faster device switching for a given thickness compared to traditional dielectric materials such as SiO2. With the advances in logic scaling, low-k materials are increasingly more prominent in the structures of advanced devices. For example, low-k materials are essential as the spacer material to provide both etch selectivity between dielectric materials and electrical isolation in field effect transistors. Consequently, the integration of low-k materials requires that the etch behavior of these materials be well understood so that the device structures can be reliably and reproducibly fabricated. In this study, the authors used a high-density plasma reactor with benchmark CF4- and NF3-based process chemistries to etch low-k materials including SiCN, SiOCN, and SiBCN in addition to Si, SiO2, and SiN reference materials. Numerous characterization techniques were utilized to understand the relationships between the plasma conditions, the evolution of the surface chemistry of the materials, and the resulting etch behavior. These techniques consisted of optical emission spectroscopy, spectroscopic ellipsometry, x-ray photoelectron spectroscopy, and attenuated total reflection Fourier transform infrared spectroscopy. The etch behavior of low-k materials under a given etch process is vital for establishing the etch selectivities in multilayer structures that are required to yield complex device geometries. For example, a directly proportional correlation was observed between the etch rate and intrinsic nitrogen concentration of the low-k materials. Potential mechanisms for the observed etch behaviors were explored using modeling and found that the intrinsic nitrogen composition in the low-k materials can result in energetically favorable reactions that result in the weakening and volatilization of the Si–N bond. Identifying the underlying mechanisms for the etch behaviors of low-k materials will provide key guidance into the development of etch processes that integrate these materials in current and future device structures.
{"title":"Significance of plasma-surface interactions in the etch behavior of low-k materials","authors":"Adam Pranda, Steven Grzeskowiak, Yu- Hao Tsai, Yusuke Yoshida, Eric Liu, Yun Han, Peter Biolsi, Ken Kobayashi, Nobuyuki Ikezawa","doi":"10.1116/6.0003014","DOIUrl":"https://doi.org/10.1116/6.0003014","url":null,"abstract":"Low-k materials are an integral component in the advancement of semiconductor device performance by reducing parasitic capacitance and enabling faster device switching for a given thickness compared to traditional dielectric materials such as SiO2. With the advances in logic scaling, low-k materials are increasingly more prominent in the structures of advanced devices. For example, low-k materials are essential as the spacer material to provide both etch selectivity between dielectric materials and electrical isolation in field effect transistors. Consequently, the integration of low-k materials requires that the etch behavior of these materials be well understood so that the device structures can be reliably and reproducibly fabricated. In this study, the authors used a high-density plasma reactor with benchmark CF4- and NF3-based process chemistries to etch low-k materials including SiCN, SiOCN, and SiBCN in addition to Si, SiO2, and SiN reference materials. Numerous characterization techniques were utilized to understand the relationships between the plasma conditions, the evolution of the surface chemistry of the materials, and the resulting etch behavior. These techniques consisted of optical emission spectroscopy, spectroscopic ellipsometry, x-ray photoelectron spectroscopy, and attenuated total reflection Fourier transform infrared spectroscopy. The etch behavior of low-k materials under a given etch process is vital for establishing the etch selectivities in multilayer structures that are required to yield complex device geometries. For example, a directly proportional correlation was observed between the etch rate and intrinsic nitrogen concentration of the low-k materials. Potential mechanisms for the observed etch behaviors were explored using modeling and found that the intrinsic nitrogen composition in the low-k materials can result in energetically favorable reactions that result in the weakening and volatilization of the Si–N bond. Identifying the underlying mechanisms for the etch behaviors of low-k materials will provide key guidance into the development of etch processes that integrate these materials in current and future device structures.","PeriodicalId":17490,"journal":{"name":"Journal of Vacuum Science & Technology A","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136347435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amorphous BxC films were deposited from the coreaction of triethylboron (TEB) and trimethylboron (TMB) at 700 °C in H2. We observed that combining both precursors allows us to balance their deposition kinetics and yields higher growth rates. Quantitative analysis by x-ray photoelectron spectroscopy shows that a wide range of B/C ratios between 0.7 and 4.1 could be obtained by varying the TEB:TMB ratio. Raman spectroscopy was used to assess the bonding in the films that gradually evolved from a structure similar to that of a-B, to a mixture of half-icosahedra embedded in a carbon matrix to a graphitic structure, as the carbon content increased. The addition of TMB in the gas phase was found to result in a decrease in elasticity and hardness but an improved adhesion, resulting in complex crack patterns upon cleaving, such as sinusoidal cracks and loops. On the one hand, the incorporation of carbon from TMB leads to an increasing contribution of the softer carbon matrix, to the detriment of polyhedral B–C structures, which in turn decreases Young’s modulus and hardness. On the other hand, it suggests that near the film-substrate interface, the presence of the carbon matrix affords a high density of strong carbon-based bonds, resulting in improved adhesion and preventing delamination of the coatings.
{"title":"Chemical vapor deposition of amorphous boron carbide coatings from mixtures of trimethylboron and triethylboron","authors":"Laurent Souqui, Hans Högberg, Henrik Pedersen","doi":"10.1116/6.0003001","DOIUrl":"https://doi.org/10.1116/6.0003001","url":null,"abstract":"Amorphous BxC films were deposited from the coreaction of triethylboron (TEB) and trimethylboron (TMB) at 700 °C in H2. We observed that combining both precursors allows us to balance their deposition kinetics and yields higher growth rates. Quantitative analysis by x-ray photoelectron spectroscopy shows that a wide range of B/C ratios between 0.7 and 4.1 could be obtained by varying the TEB:TMB ratio. Raman spectroscopy was used to assess the bonding in the films that gradually evolved from a structure similar to that of a-B, to a mixture of half-icosahedra embedded in a carbon matrix to a graphitic structure, as the carbon content increased. The addition of TMB in the gas phase was found to result in a decrease in elasticity and hardness but an improved adhesion, resulting in complex crack patterns upon cleaving, such as sinusoidal cracks and loops. On the one hand, the incorporation of carbon from TMB leads to an increasing contribution of the softer carbon matrix, to the detriment of polyhedral B–C structures, which in turn decreases Young’s modulus and hardness. On the other hand, it suggests that near the film-substrate interface, the presence of the carbon matrix affords a high density of strong carbon-based bonds, resulting in improved adhesion and preventing delamination of the coatings.","PeriodicalId":17490,"journal":{"name":"Journal of Vacuum Science & Technology A","volume":"10 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136346258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Montalvo-Urquizo, D. A. Mazón-Montijo, A. A. Ortíz-Atondo, A. L. Martínez-García, M. I. Mendivil-Palma, O. Y. Ramírez-Esquivel, Z. Montiel-González
Semiconductor thin films and coatings have become one of the most relevant research fields due to their significant applications in priority energy-related technologies such as solar cells, photocatalysts, and smart windows. Since all these fields are conceived as tools to fight against the effects of climate change, a real impact requires the successful deposition of semiconductor films on large-area substrates such as windows, panels, pipes, and containers, to give rise to photoactive components suitable for buildings, industries, cars, and parks. However, scalability remains one of the major issues in almost all methodologies known for the deposition of semiconductor films, irrespective of the phase approach used, i.e., either from vapor- or liquid-phase. Here, a mathematical metamodel was applied to simulate the atomic layer deposition (ALD) of zinc oxide (ZnO) ultrathin films (a versatile photoactive material in energy-related research) and optimized their thickness and homogeneity over the whole area of 8 in.-diameter Si wafers. Knowing all ALD parameters that define the quality and properties of the deposited films, we delimitated a set of four metamodel-inputs (zinc precursor dose, purge, and the inner and outer carrier gas flows) based on literature review, expertise, costs, and reactor design aspects specific to the deposition of ZnO. The average thickness and homogeneity of the films were established as the two outputs of the metamodel, which were the object of optimization. Using advanced iterative procedures, we carried out three rounds of experiments that lead us to a set of ALD parameters to deposit a ZnO ultrathin film with an average thickness of 11.38 nm that leads to a deposition rate of 1.9 Å/cycle, which represents 90% of the highest reported value for ZnO by ALD (2.1 Å/cycle). The homogeneity over the whole 8 in.-diameter wafer reached 2.61 nm, which represents the smoothest distribution of thickness values in the entire deposited area. Given the origin of the limits constraining this optimization procedure, our results hold promise in supporting the transition from the laboratory-level synthesis of thin-film-based optoelectronic devices to their large-scale production. This could ultimately help to circumvent the difficulties faced in scaling the ALD technology and enable alternative deposition methodologies such as thermal ALD, otherwise inaccessible to the production chain.
{"title":"Advanced two-objective optimization of thickness and large-area homogeneity of ZnO ultrathin films deposited by atomic layer deposition","authors":"J. Montalvo-Urquizo, D. A. Mazón-Montijo, A. A. Ortíz-Atondo, A. L. Martínez-García, M. I. Mendivil-Palma, O. Y. Ramírez-Esquivel, Z. Montiel-González","doi":"10.1116/6.0002829","DOIUrl":"https://doi.org/10.1116/6.0002829","url":null,"abstract":"Semiconductor thin films and coatings have become one of the most relevant research fields due to their significant applications in priority energy-related technologies such as solar cells, photocatalysts, and smart windows. Since all these fields are conceived as tools to fight against the effects of climate change, a real impact requires the successful deposition of semiconductor films on large-area substrates such as windows, panels, pipes, and containers, to give rise to photoactive components suitable for buildings, industries, cars, and parks. However, scalability remains one of the major issues in almost all methodologies known for the deposition of semiconductor films, irrespective of the phase approach used, i.e., either from vapor- or liquid-phase. Here, a mathematical metamodel was applied to simulate the atomic layer deposition (ALD) of zinc oxide (ZnO) ultrathin films (a versatile photoactive material in energy-related research) and optimized their thickness and homogeneity over the whole area of 8 in.-diameter Si wafers. Knowing all ALD parameters that define the quality and properties of the deposited films, we delimitated a set of four metamodel-inputs (zinc precursor dose, purge, and the inner and outer carrier gas flows) based on literature review, expertise, costs, and reactor design aspects specific to the deposition of ZnO. The average thickness and homogeneity of the films were established as the two outputs of the metamodel, which were the object of optimization. Using advanced iterative procedures, we carried out three rounds of experiments that lead us to a set of ALD parameters to deposit a ZnO ultrathin film with an average thickness of 11.38 nm that leads to a deposition rate of 1.9 Å/cycle, which represents 90% of the highest reported value for ZnO by ALD (2.1 Å/cycle). The homogeneity over the whole 8 in.-diameter wafer reached 2.61 nm, which represents the smoothest distribution of thickness values in the entire deposited area. Given the origin of the limits constraining this optimization procedure, our results hold promise in supporting the transition from the laboratory-level synthesis of thin-film-based optoelectronic devices to their large-scale production. This could ultimately help to circumvent the difficulties faced in scaling the ALD technology and enable alternative deposition methodologies such as thermal ALD, otherwise inaccessible to the production chain.","PeriodicalId":17490,"journal":{"name":"Journal of Vacuum Science & Technology A","volume":" 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135242475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Plasma etching effects, such as microtrenching and bowing, negatively impact device performance. Modeling of these effects at nanoscale is challenging, and theoretical and experimental investigations are highly desired to gain insights into mechanisms. In this paper, we propose a new plasma etching model based on Monte Carlo simulations with a cellular method. This model considers reactions and ion-enhanced etching and consists of a novel particle reflection algorithm, which is a key factor impacting the etch profile. This model reproduces the adjustable microtrenching and bowing effects in periodic dense trenches with tens of nanometer dimensions. We conduct experiments of Si etching by Cl2 and validate the model by comparing the simulated profile with cross-sectional scanning electron microscope images. This work enables a potential physical model driven process emulation tool toward design technology co-optimization.
{"title":"Modeling of microtrenching and bowing effects in nanoscale Si inductively coupled plasma etching process","authors":"Ziyi Hu, Hua Shao, Junjie Li, Panpan Lai, Wenrui Wang, Chen Li, Qi Yan, Xiaobin He, Junfeng Li, Tao Yang, Rui Chen, Yayi Wei","doi":"10.1116/6.0003032","DOIUrl":"https://doi.org/10.1116/6.0003032","url":null,"abstract":"Plasma etching effects, such as microtrenching and bowing, negatively impact device performance. Modeling of these effects at nanoscale is challenging, and theoretical and experimental investigations are highly desired to gain insights into mechanisms. In this paper, we propose a new plasma etching model based on Monte Carlo simulations with a cellular method. This model considers reactions and ion-enhanced etching and consists of a novel particle reflection algorithm, which is a key factor impacting the etch profile. This model reproduces the adjustable microtrenching and bowing effects in periodic dense trenches with tens of nanometer dimensions. We conduct experiments of Si etching by Cl2 and validate the model by comparing the simulated profile with cross-sectional scanning electron microscope images. This work enables a potential physical model driven process emulation tool toward design technology co-optimization.","PeriodicalId":17490,"journal":{"name":"Journal of Vacuum Science & Technology A","volume":"76 5‐6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135341575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yufan Li, Yong Zheng, Yuze Du, Xi Zhang, Wangwang Wang, Jin Lv
The antireflective coating (ARC) is fabricated by the sol-gel method using mixed sol modified by Si–Ti composite sol. The effects of the mixing ratio of Si–Ti composite sol and hollow silica sol on the surface morphology, optical properties, mechanical properties, and wetting ability of the ARC were studied. Moreover, the self-cleaning ability and environmental stability were examined via dip coating the modified sol on glass substrates. The proposed ARC exhibited a total solar-weighted transmittance (Тsw) of more than 94.97% over a wavelength range of 380–1100 nm, significantly higher than that of the bare glass substrate (Тsw = 90.62%). After modification, the proposed ARC exhibited a hardness of 3 H. In addition, the coating presented an extremely hydrophilic surface with a minimum water contact angle of less than 5°. Water droplets resulted in the formation of a water film on the ARC surface, which could significantly reduce the adverse effects of subsequent pollutants on the coating transmittance; simultaneously, owing to the introduction of TiO2, the coating could oxidatively decompose organic contamination. Finally, damp test results showed that the ARC transmittance only decreased by 0.05%, indicating good environmental stability.
{"title":"Fabrication of antireflective coatings with self-cleaning function using Si–Ti modified hollow silicon mixed sol","authors":"Yufan Li, Yong Zheng, Yuze Du, Xi Zhang, Wangwang Wang, Jin Lv","doi":"10.1116/6.0003082","DOIUrl":"https://doi.org/10.1116/6.0003082","url":null,"abstract":"The antireflective coating (ARC) is fabricated by the sol-gel method using mixed sol modified by Si–Ti composite sol. The effects of the mixing ratio of Si–Ti composite sol and hollow silica sol on the surface morphology, optical properties, mechanical properties, and wetting ability of the ARC were studied. Moreover, the self-cleaning ability and environmental stability were examined via dip coating the modified sol on glass substrates. The proposed ARC exhibited a total solar-weighted transmittance (Тsw) of more than 94.97% over a wavelength range of 380–1100 nm, significantly higher than that of the bare glass substrate (Тsw = 90.62%). After modification, the proposed ARC exhibited a hardness of 3 H. In addition, the coating presented an extremely hydrophilic surface with a minimum water contact angle of less than 5°. Water droplets resulted in the formation of a water film on the ARC surface, which could significantly reduce the adverse effects of subsequent pollutants on the coating transmittance; simultaneously, owing to the introduction of TiO2, the coating could oxidatively decompose organic contamination. Finally, damp test results showed that the ARC transmittance only decreased by 0.05%, indicating good environmental stability.","PeriodicalId":17490,"journal":{"name":"Journal of Vacuum Science & Technology A","volume":"28 31","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135390948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cheng Liu, Nikhil Pokharel, Qinchen Lin, Miguel A. Betancourt Ponce, Jian Sun, Dominic Lane, Thomas J. De Prinse, Nelson Tansu, Padma Gopalan, Chirag Gupta, Shubhra S. Pasayat, Luke J. Mawst
In this study, the selective area epitaxy (SAE) of InGaN/GaN nanopyramid quantum dots (QDs) on a block copolymer patterned (BCP) GaN template using metalorganic chemical vapor deposition is reported. The pattern transfer process and SAE process are developed to enable a ultrahigh density of 7–9 × 1010 cm−2 QD formation with a feature size of 20–35 nm. The growth mechanism and geometrical properties of the QDs were investigated by scanning electron microscopy and cross-sectional transmission electron microscopy, showing the nanopyramid QD structure with InGaN grown on semipolar {101¯1} planes. The optical characteristics of the nanopyramid QDs were examined by microphotoluminescence measurements. We observed QD emission centered at 488 and 514 nm, depending on the growth temperature employed. These emissions were found to be longer wavelength than those from a planar quantum well structure. This can be attributed to the combined effects of higher indium incorporation along the semipolar plane and a larger InGaN thickness. Furthermore, we also found that the QD emission intensity increases as the number of InGaN layers increases without wavelength shift, indicating a constant growth rate and indium incorporation along the semipolar plane after the formation of the nanopyramid structure. The internal quantum efficiency is estimated to be over 60% by comparing the photoluminescence (PL) intensity of QDs at low temperature and room temperature. PL emission wavelength shows an 11 nm blue shift, while the full width at half maximum decreases from 68 (351 meV) to 56 nm (303 meV) from room temperature to low temperature. By employing BCP lithography and SAE technique, we successfully demonstrated that ultrahigh density, small size InGaN/GaN nanopyramid QDs with visible emission were achieved, which could be a potential active region for QD light-emitting diodes and/or lasers.
{"title":"Ultrahigh density InGaN/GaN nanopyramid quantum dots for visible emissions with high quantum efficiency","authors":"Cheng Liu, Nikhil Pokharel, Qinchen Lin, Miguel A. Betancourt Ponce, Jian Sun, Dominic Lane, Thomas J. De Prinse, Nelson Tansu, Padma Gopalan, Chirag Gupta, Shubhra S. Pasayat, Luke J. Mawst","doi":"10.1116/6.0002997","DOIUrl":"https://doi.org/10.1116/6.0002997","url":null,"abstract":"In this study, the selective area epitaxy (SAE) of InGaN/GaN nanopyramid quantum dots (QDs) on a block copolymer patterned (BCP) GaN template using metalorganic chemical vapor deposition is reported. The pattern transfer process and SAE process are developed to enable a ultrahigh density of 7–9 × 1010 cm−2 QD formation with a feature size of 20–35 nm. The growth mechanism and geometrical properties of the QDs were investigated by scanning electron microscopy and cross-sectional transmission electron microscopy, showing the nanopyramid QD structure with InGaN grown on semipolar {101¯1} planes. The optical characteristics of the nanopyramid QDs were examined by microphotoluminescence measurements. We observed QD emission centered at 488 and 514 nm, depending on the growth temperature employed. These emissions were found to be longer wavelength than those from a planar quantum well structure. This can be attributed to the combined effects of higher indium incorporation along the semipolar plane and a larger InGaN thickness. Furthermore, we also found that the QD emission intensity increases as the number of InGaN layers increases without wavelength shift, indicating a constant growth rate and indium incorporation along the semipolar plane after the formation of the nanopyramid structure. The internal quantum efficiency is estimated to be over 60% by comparing the photoluminescence (PL) intensity of QDs at low temperature and room temperature. PL emission wavelength shows an 11 nm blue shift, while the full width at half maximum decreases from 68 (351 meV) to 56 nm (303 meV) from room temperature to low temperature. By employing BCP lithography and SAE technique, we successfully demonstrated that ultrahigh density, small size InGaN/GaN nanopyramid QDs with visible emission were achieved, which could be a potential active region for QD light-emitting diodes and/or lasers.","PeriodicalId":17490,"journal":{"name":"Journal of Vacuum Science & Technology A","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135479876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this work, we first review the previous work done on statistical nanoindentation by different researchers, highlighting the main problems that have been found and possible proposed solutions. In the second part, we study and report the statistical nanoindentation of three model samples, in the form of a soft Al2124 matrix embedded with hard SiC particles. Three different variants were selected: (1) 25% of SiC particles with 3 μm diameter; (2) 25% of SiC particles with 0.7 μm diameter; and (3) 17% of SiC particles with 0.3 μm diameter. We propose a novel heuristic wavelet technique to filter the measurement noise from the raw nanoindentation data as an attempt to obtain a more robust statistical nanoindentation methodology. Our results have shown that, when the nanoindentation data are filtered, it is not necessary to select a priori the number of peaks (phases) to be analyzed and, in some cases, a wide number of bin-sizes can be used without affecting the results. Finally, a finite element modeling have been used to analyze the response of the nanoindenter regarding the position of the hard particle. Our model shows that it is impossible to get the whole hardness value of the hard SiC particle by the statistical nanoindentation methodology.
{"title":"Study of Al2124-SiC nanocomposites by an improved statistical nanoindentation methodology","authors":"Esteban Broitman, Yuri Kadin, Predrag Andric","doi":"10.1116/6.0003048","DOIUrl":"https://doi.org/10.1116/6.0003048","url":null,"abstract":"In this work, we first review the previous work done on statistical nanoindentation by different researchers, highlighting the main problems that have been found and possible proposed solutions. In the second part, we study and report the statistical nanoindentation of three model samples, in the form of a soft Al2124 matrix embedded with hard SiC particles. Three different variants were selected: (1) 25% of SiC particles with 3 μm diameter; (2) 25% of SiC particles with 0.7 μm diameter; and (3) 17% of SiC particles with 0.3 μm diameter. We propose a novel heuristic wavelet technique to filter the measurement noise from the raw nanoindentation data as an attempt to obtain a more robust statistical nanoindentation methodology. Our results have shown that, when the nanoindentation data are filtered, it is not necessary to select a priori the number of peaks (phases) to be analyzed and, in some cases, a wide number of bin-sizes can be used without affecting the results. Finally, a finite element modeling have been used to analyze the response of the nanoindenter regarding the position of the hard particle. Our model shows that it is impossible to get the whole hardness value of the hard SiC particle by the statistical nanoindentation methodology.","PeriodicalId":17490,"journal":{"name":"Journal of Vacuum Science & Technology A","volume":"45 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135819712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Miu Lun Lau, Abraham Burleigh, Jeff Terry, Min Long
Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.
{"title":"Materials characterization: Can artificial intelligence be used to address reproducibility challenges?","authors":"Miu Lun Lau, Abraham Burleigh, Jeff Terry, Min Long","doi":"10.1116/6.0002809","DOIUrl":"https://doi.org/10.1116/6.0002809","url":null,"abstract":"Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.","PeriodicalId":17490,"journal":{"name":"Journal of Vacuum Science & Technology A","volume":"65 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135868479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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