Influence of deposition time on absorption and electrical characteristics of ZnS thin films

Hathot, Saba Farhan; Abbas, Sabah Ibrahim; Thjeel, Hanan A.; Salim, A.A.

doi:10.1016/j.ijleo.2022.169056

Cited by 15 publications

(9 citation statements)

References 31 publications

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“…Not only are the features of the protocol something that must be taken into consideration by implementers of southbound support, but also possible extensions, future versions, and other variables. For instance, in the case of OpenFlow, the option to communicate via IPv6 was left out of OpenFlow v1.0 but was included to the OpenFlow v1.3 standard [27].…”

Section: B Support From the Southmentioning

confidence: 99%

A Useful and Effective Method for Selecting a Smart Controller for SDN Network Design and Implement

Rashid,

Shiltagh Al-Jamali

2023

Int J Innov Comp

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Software Defined Networking (SDN) is a modern network architectural model that manages network traffic using software. SDN is a networking scenario that modifies the conventional network design by combining all control features into a single place and making all choices centrally. Controllers are the "brains" of SDN architecture since they are responsible for making control decisions and routing packets at the same time. The capacity for centralized decision-making on routing improves the performance of the network. SDN's growing functionality and uses have led to the development of many controller systems. Every SDN controller idea or design must prioritize the control plane since it is the most crucial part of the SDN architecture. Studies have been done to examine, analyze, and evaluate the relative advantages of the many controllers that have been created in recent years. In this paper, finding the perfect controller based on derived needs (for example, the controller must have a "Java" or "Python" interface), a matching process compares controller features with requirements.

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Section: B Support From the Southmentioning

confidence: 99%

A Useful and Effective Method for Selecting a Smart Controller for SDN Network Design and Implement

Rashid,

Shiltagh Al-Jamali

2023

Int J Innov Comp

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“…In addition to the gold standard of a skin biopsy, dermatologists may utilize a variety of quantitative marking approaches, such as the "ABCD" rule, 7-point checklist and 3-point checklist [3], to detect melanoma at an early stage. Recently, computer vision, ML, and DL have been used to melanoma detection with great success [7][8][9][10][11]. Using a suggested CNN architecture and benchmark datasets [12], this paper applies deep learning to the problem of detecting malignant melanoma.…”

Section: Introductionmentioning

confidence: 99%

Melanoma Skin Cancer Classification based on CNN Deep Learning Algorithms

Waheed

Saadi

Rahim

et al. 2023

Mal. J. Fund. Appl. Sci.

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Melanoma, the deadliest form of skin cancer, is on the rise. The goal of this study is to present a deep learning system implementation for the detection of melanoma lesions on a server equipped with a graphics processing unit (GPU). When applied by a dermatologist, the recommended method might aid in the early detection of this kind of skin cancer. Evidence shows that deep learning may be used in a variety of settings to successfully extract patterns from data such as signals and images. This research presents a convolution neural network–based strategy for identifying early-stage melanoma skin cancer. Images are input into a deep learning model known as a convolutional neural network (CNN) that has already been pre-trained. The CNN classifier, which is trained with large amounts of data, can discriminate between malignant and nonmalignant melanoma. The method's success in the lab bodes well for its potential to aid dermatologists in the early detection of melanoma. However, the experimental results show that the proposed technique excels beyond the state-of-the-art methods in terms of diagnostic accuracy.

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“…Nowadays, because of its high bandgap energy and good optical properties, ZnS nanomaterials have attracted more and more research interest, and have been widely used in the manufacture of semiconductor lasers, UV and IR light detectors, IR lens, IR window, and solar cells. [6][7][8][9][10] In addition, 99.99% high purity crystal ZnS is widely used in civil, military, aerospace and other high-tech fields of important basic materials, but also extremely important in the long-wave far-IR precision guidance, far-IR imaging window materials, especially in the national defense, military and other cutting-edge science and technology fields. Because of its wear resistance, high strength, excellent far-IR laser beam characteristics, it can be widely used in aerospace, IR, detection, electronics and other industries.…”

Section: Introductionmentioning

confidence: 99%

Characterization of ZnS films on different substrates by pulsed laser deposition

Wang,

Xing,

Feng

et al. 2023

Jpn. J. Appl. Phys.

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ZnS films were deposited on Si and quartz substrates via pulsed laser deposition, and the structure, morphology and photoelectric characteristics of as-deposited and annealed ZnS films were compared. XRD results show that ZnS films are polycrystalline, and the intensity of peak at 28.4° on Si is larger, with a narrower FWHM. AFM topographies show that the surface of ZnS films becomes rough after annealing, and the roughness on quartz is obviously larger than that on Si. The transmittance of ZnS films in the visible light region reaches 80%, which indicates that ZnS is suitable for making transparent conductive films. PL spectra show that there are two luminescence peaks at 425 and 445 nm related to the intrinsic defect levels of ZnS. Due to the small lattice mismatch, PL intensity on Si is significantly weaker than that on quartz. I-V characteristics of ZnS/p-Si heterojunctions show typical p-n junction diode-like rectification behavior.

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Influence of deposition time on absorption and electrical characteristics of ZnS thin films

Cited by 15 publications

References 31 publications

A Useful and Effective Method for Selecting a Smart Controller for SDN Network Design and Implement

A Useful and Effective Method for Selecting a Smart Controller for SDN Network Design and Implement

Melanoma Skin Cancer Classification based on CNN Deep Learning Algorithms

Characterization of ZnS films on different substrates by pulsed laser deposition

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