Preparation of wafer-scale highly conformalamorphous hafnium dioxide thin films by atomic layer deposition using a thermally stable boratabenzene ligand-containing hafnium precursor

Ansari, Mohd Zahid; Janíček, Petr; Park, Ye Jin; NamGung, Sook; Cho, Bo Yeon; Nandi, Dip K.; Jang, Yujin; Bae, Jong Seong; Hong, Tae Eun; Cheon, Taehoon; Song, Wooseok; Ahn, Ki-Seok; Kim, Soohyun

doi:10.1016/j.apsusc.2023.156834

Cited by 4 publications

(1 citation statement)

References 69 publications

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“…The presence of different defective categories on the same wafer or in a specific area is called the mixed-type nature of wafer defects. That renders the flaw identification technique more difficult because it may not be possible for one flaw identification method to correctly identify and categorize every type of problem (Ansari et al 2023). Improved computer vision, machine learning, and other algorithms must be used along with numerous methodologies for the successful identification of mixed-type faults.…”

Section: Introductionmentioning

confidence: 99%

Efficient mixed-type wafer defect pattern recognition using a novel joint attention skipped graph convolutional neural network

Natesan,

Ahmed,

Singhal

2023

Multidiscip. Sci. J.

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For wafer foundries to maintain high yield and quality, errors in manufacturing wafers must be identified and corrected. To find the source of problems and increase overall yield, defect pattern recognition (DPR) is essential. Nevertheless, mixed-type DPR, which includes identifying numerous types of flaws at once, poses more difficulties because of the variety of spatial features, the unpredictability of flaw patterns, and the fluctuating amount of flaws present. So, a novel joint attention skipped graph convolutional neural network (JAS-GCNN) technique is suggested in this paper to analyse the difficulty of mixed-type DPR. The JAS- GCNN effectively captures locally as well as globally interconnections among faults on a wafer by fusing the strength of GCNN along with focused processes. The JAS-GCNN facilitates the learning of complicated fault patterns by allowing information to propagate across multiple layers by utilising skip connections. We do trials on the MixedWM38 WaferMap dataset, which contains a variety of defect types, to assess the proposed technique. The outcomes show that when compared to cutting-edge techniques, the JAS-GCNN performs better. It successfully detects various faults and performs defect classification jobs with high accuracy.

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Section: Introductionmentioning

confidence: 99%

Efficient mixed-type wafer defect pattern recognition using a novel joint attention skipped graph convolutional neural network

Natesan,

Ahmed,

Singhal

2023

Multidiscip. Sci. J.

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Atomic Layer Deposition—A Versatile Toolbox for Designing/Engineering Electrodes for Advanced Supercapacitors

Ansari,

Hussain,

Mohapatra

et al. 2023

Advanced Science

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Atomic layer deposition (ALD) has become the most widely used thin‐film deposition technique in various fields due to its unique advantages, such as self‐terminating growth, precise thickness control, and excellent deposition quality. In the energy storage domain, ALD has shown great potential for supercapacitors (SCs) by enabling the construction and surface engineering of novel electrode materials. This review aims to present a comprehensive outlook on the development, achievements, and design of advanced electrodes involving the application of ALD for realizing high‐performance SCs to date, as organized in several sections of this paper. Specifically, this review focuses on understanding the influence of ALD parameters on the electrochemical performance and discusses the ALD of nanostructured electrochemically active electrode materials on various templates for SCs.It examines the influence of ALD parameters on electrochemical performance and highlights ALD's role in passivating electrodes and creating 3D nanoarchitectures. The relationship between synthesis procedures and SC properties is analyzed to guide future research in preparing materials for various applications. Finally, it is concluded by suggesting the directions and scope of future research and development to further leverage the unique advantages of ALD for fabricating new materials and harness the unexplored opportunities in the fabrication of advanced‐generation SCs.

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The Roadmap of 2D Materials and Devices Toward Chips

Liu,

Zhang,

Liu

et al. 2024

Nano-Micro Lett.

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Due to the constraints imposed by physical effects and performance degradation, silicon-based chip technology is facing certain limitations in sustaining the advancement of Moore’s law. Two-dimensional (2D) materials have emerged as highly promising candidates for the post-Moore era, offering significant potential in domains such as integrated circuits and next-generation computing. Here, in this review, the progress of 2D semiconductors in process engineering and various electronic applications are summarized. A careful introduction of material synthesis, transistor engineering focused on device configuration, dielectric engineering, contact engineering, and material integration are given first. Then 2D transistors for certain electronic applications including digital and analog circuits, heterogeneous integration chips, and sensing circuits are discussed. Moreover, several promising applications (artificial intelligence chips and quantum chips) based on specific mechanism devices are introduced. Finally, the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed, and potential development pathways or roadmaps are further speculated and outlooked.

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Preparation of wafer-scale highly conformalamorphous hafnium dioxide thin films by atomic layer deposition using a thermally stable boratabenzene ligand-containing hafnium precursor

Cited by 4 publications

References 69 publications

Efficient mixed-type wafer defect pattern recognition using a novel joint attention skipped graph convolutional neural network

Efficient mixed-type wafer defect pattern recognition using a novel joint attention skipped graph convolutional neural network

Atomic Layer Deposition—A Versatile Toolbox for Designing/Engineering Electrodes for Advanced Supercapacitors

The Roadmap of 2D Materials and Devices Toward Chips

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