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Wilhelmer C, Waldhoer D, Cvitkovich L, Milardovich D, Waltl M, Grasser T. Over- and Undercoordinated Atoms as a Source of Electron and Hole Traps in Amorphous Silicon Nitride (a-Si 3N 4). Nanomaterials (Basel) 2023; 13:2286. [PMID: 37630870 PMCID: PMC10460034 DOI: 10.3390/nano13162286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 07/31/2023] [Accepted: 08/04/2023] [Indexed: 08/27/2023]
Abstract
Silicon nitride films are widely used as the charge storage layer of charge trap flash (CTF) devices due to their high charge trap densities. The nature of the charge trapping sites in these materials responsible for the memory effect in CTF devices is still unclear. Most prominently, the Si dangling bond or K-center has been identified as an amphoteric trap center. Nevertheless, experiments have shown that these dangling bonds only make up a small portion of the total density of electrical active defects, motivating the search for other charge trapping sites. Here, we use a machine-learned force field to create model structures of amorphous Si3N4 by simulating a melt-and-quench procedure with a molecular dynamics algorithm. Subsequently, we employ density functional theory in conjunction with a hybrid functional to investigate the structural properties and electronic states of our model structures. We show that electrons and holes can localize near over- and under-coordinated atoms, thereby introducing defect states in the band gap after structural relaxation. We analyze these trapping sites within a nonradiative multi-phonon model by calculating relaxation energies and thermodynamic charge transition levels. The resulting defect parameters are used to model the potential energy curves of the defect systems in different charge states and to extract the classical energy barrier for charge transfer. The high energy barriers for charge emission compared to the vanishing barriers for charge capture at the defect sites show that intrinsic electron traps can contribute to the memory effect in charge trap flash devices.
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Affiliation(s)
- Christoph Wilhelmer
- Christian Doppler Laboratory for Single-Defect Spectroscopy in Semiconductor Devices, Institute for Microelectronics, TU Wien, 1040 Wien, Austria
- Institute for Microelectronics, TU Wien, Gusshausstrasse 27-29, 1040 Wien, Austria (T.G.)
| | - Dominic Waldhoer
- Institute for Microelectronics, TU Wien, Gusshausstrasse 27-29, 1040 Wien, Austria (T.G.)
| | - Lukas Cvitkovich
- Institute for Microelectronics, TU Wien, Gusshausstrasse 27-29, 1040 Wien, Austria (T.G.)
| | - Diego Milardovich
- Institute for Microelectronics, TU Wien, Gusshausstrasse 27-29, 1040 Wien, Austria (T.G.)
| | - Michael Waltl
- Christian Doppler Laboratory for Single-Defect Spectroscopy in Semiconductor Devices, Institute for Microelectronics, TU Wien, 1040 Wien, Austria
| | - Tibor Grasser
- Institute for Microelectronics, TU Wien, Gusshausstrasse 27-29, 1040 Wien, Austria (T.G.)
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Ravichandran H, Knobloch T, Pannone A, Karl A, Stampfer B, Waldhoer D, Zheng Y, Sakib NU, Karim Sadaf MU, Pendurthi R, Torsi R, Robinson JA, Grasser T, Das S. Observation of Rich Defect Dynamics in Monolayer MoS 2. ACS Nano 2023. [PMID: 37490390 DOI: 10.1021/acsnano.2c12900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Defects play a pivotal role in limiting the performance and reliability of nanoscale devices. Field-effect transistors (FETs) based on atomically thin two-dimensional (2D) semiconductors such as monolayer MoS2 are no exception. Probing defect dynamics in 2D FETs is therefore of significant interest. Here, we present a comprehensive insight into various defect dynamics observed in monolayer MoS2 FETs at varying gate biases and temperatures. The measured source-to-drain currents exhibit random telegraph signals (RTS) owing to the transfer of charges between the semiconducting channel and individual defects. Based on the modeled temperature and gate bias dependence, oxygen vacancies or aluminum interstitials are probable defect candidates. Several types of RTSs are observed including anomalous RTS and giant RTS indicating local current crowding effects and rich defect dynamics in monolayer MoS2 FETs. This study explores defect dynamics in large area-grown monolayer MoS2 with ALD-grown Al2O3 as the gate dielectric.
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Affiliation(s)
- Harikrishnan Ravichandran
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Theresia Knobloch
- Institute for Microelectronics (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
| | - Andrew Pannone
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Alexander Karl
- Institute for Microelectronics (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
| | - Bernhard Stampfer
- Institute for Microelectronics (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
| | - Dominic Waldhoer
- Institute for Microelectronics (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
| | - Yikai Zheng
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Najam U Sakib
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Muhtasim Ul Karim Sadaf
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Rahul Pendurthi
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Riccardo Torsi
- Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
| | - Joshua A Robinson
- Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, United States
- Department of Physics, Penn State University, University Park, Pennsylvania 16802, United States
| | - Tibor Grasser
- Institute for Microelectronics (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
| | - Saptarshi Das
- Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States
- Materials Science and Engineering, Penn State University, University Park, Pennsylvania 16802, United States
- Materials Research Institute, Penn State University, University Park, Pennsylvania 16802, United States
- Electrical Engineering, Penn State University, University Park, Pennsylvania 16802, United States
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Milardovich D, Wilhelmer C, Waldhoer D, Cvitkovich L, Sivaraman G, Grasser T. Machine learning interatomic potential for silicon-nitride (Si3N4) by active learning. J Chem Phys 2023; 158:2890479. [PMID: 37184017 DOI: 10.1063/5.0146753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 04/20/2023] [Indexed: 05/16/2023] Open
Abstract
Silicon nitride (Si3N4) is an extensively used material in the automotive, aerospace, and semiconductor industries. However, its widespread use is in contrast to the scarce availability of reliable interatomic potentials that can be employed to study various aspects of this material on an atomistic scale, particularly its amorphous phase. In this work, we developed a machine learning interatomic potential, using an efficient active learning technique, combined with the Gaussian approximation potential (GAP) method. Our strategy is based on using an inexpensive empirical potential to generate an initial dataset of atomic configurations, for which energies and forces were recalculated with density functional theory (DFT); thereafter, a GAP was trained on these data and an iterative re-training algorithm was used to improve it by learning on-the-fly. When compared to DFT, our potential yielded a mean absolute error of 8 meV/atom in energy calculations for a variety of liquid and amorphous structures and a speed-up of molecular dynamics simulations by 3-4 orders of magnitude, while achieving a first-rate agreement with experimental results. Our potential is publicly available in an open-access repository.
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Affiliation(s)
- Diego Milardovich
- Institute for Microelectronics, Technische Universität Wien, Gußhausstraße 27-29, 1040 Vienna, Austria
| | - Christoph Wilhelmer
- Institute for Microelectronics, Technische Universität Wien, Gußhausstraße 27-29, 1040 Vienna, Austria
- Christian Doppler Laboratory for Single-Defect Spectroscopy in Semiconductor Devices at the Institute for Microelectronics, TU Wien, 1040 Vienna, Austria
| | - Dominic Waldhoer
- Institute for Microelectronics, Technische Universität Wien, Gußhausstraße 27-29, 1040 Vienna, Austria
| | - Lukas Cvitkovich
- Institute for Microelectronics, Technische Universität Wien, Gußhausstraße 27-29, 1040 Vienna, Austria
| | - Ganesh Sivaraman
- Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois 60439, USA
| | - Tibor Grasser
- Institute for Microelectronics, Technische Universität Wien, Gußhausstraße 27-29, 1040 Vienna, Austria
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