Demonstration of CMOS-Compatible Multi-Level Graphene Interconnects With Metal Vias

Agashiwala, Kunjesh; Jiang, Jie; Parto, Kamyar; Zhang, Dujiao; Yeh, Chao‐Hui; Banerjee, Kaustav

doi:10.1109/ted.2021.3061637

Cited by 11 publications

(4 citation statements)

References 39 publications

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“…This RTD behavior employing graphene was theoretically proposed and demonstrated in, [ 303 ] where a Gr NDR device ( Figure a) with suitably designed width for the source/drain and the channel regions offered a suitable band‐alignment (Figure 12b) resulting in large peak‐valley current ratios (PVCR) (Figure 12c) exceeding 10 14 , essential in digital applications. Moreover, the relatively low contact resistance of metal‐Gr contacts [ 304 ] and progress on fabricating Gr nanoribbons [ 292 ] make this concept realizable in the future. A RTD fabricated on vertical MoS 2 –WSe 2 heterojunctions was reported, [ 305 ] where a room‐temperature PVCR of ≈2.2 was obtained.…”

Section: Quantum Mechanical Tunneling Devices For Logic and Memorymentioning

confidence: 99%

Quantum‐Engineered Devices Based on 2D Materials for Next‐Generation Information Processing and Storage

et al. 2022

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As an approximation to the quantum state of solids, the band theory, developed nearly seven decades ago, fostered the advance of modern integrated solid‐state electronics, one of the most successful technologies in the history of human civilization. Nonetheless, their rapidly growing energy consumption and accompanied environmental issues call for more energy‐efficient electronics and optoelectronics, which necessitate the exploration of more advanced quantum mechanical effects, such as band‐to‐band tunneling, spin–orbit coupling, spin–valley locking, and quantum entanglement. The emerging 2D layered materials, featured by their exotic electrical, magnetic, optical, and structural properties, provide a revolutionary low‐dimensional and manufacture‐friendly platform (and many more opportunities) to implement these quantum‐engineered devices, compared to the traditional electronic materials system. Here, the progress in quantum‐engineered devices is reviewed and the opportunities/challenges of exploiting 2D materials are analyzed to highlight their unique quantum properties that enable novel energy‐efficient devices, and useful insights to quantum device engineers and 2D‐material scientists are provided.

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Section: Quantum Mechanical Tunneling Devices For Logic and Memorymentioning

confidence: 99%

Quantum‐Engineered Devices Based on 2D Materials for Next‐Generation Information Processing and Storage

et al. 2022

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“…To fabricate multilevel MLG interconnects, low-resistance vias are required, and metal contact, such as Co, to the MLG interconnect edge is considered to be promising. 63) Furthermore, process optimization for the via-etching and cleaning will be necessary to avoid desorption of intercalates during the processes.…”

Section: Further Issues and Prospectsmentioning

confidence: 99%

Advances in multilayer graphene processes for metallization and high-frequency devices

Ueno

2022

Jpn. J. Appl. Phys.

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Multilayer graphene (MLG) has been expected as an alternative material for nanometer wide interconnects. However, it has not been put into practical use, since the process technology that leads to practical use has been immature. Recent advances in the MLG processes and applications such as MLG capped copper interconnects, direct deposition of MLG by solid-phase deposition at a low-temperature, stable intercalation doping to MLG, and selective CVD of high crystallinity MLG for inductor and antenna applications are reviewed. According to these advances, MLG is considered to be approaching to the practical applications for device metallization and high frequency devices. Based on the characteristics of MLG as a conductor and recent development trends, the prospects and issues for future practical use of MLG graphene are discussed.

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“…While further scaling down the feature size, the researchers are confronting major challenges not only from the CMOS design but also due to interconnects, some of which are directly imposed due to quantum effects [11]. To solve this problem, several other technologies are being explored such as nanowires, nanotubes [12], [13], [14], [15], [16], [17], Quantum wires [18], [19], [17], [20], etc. other than metallic interconnects [21].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for Modeling and Performance Analysis of Interconnects

Tripathi¹,

Vaghasiya²,

Junjariya³

et al. 2021

IEEE Open J. Nanotechnol.

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Interconnects are essential components of any electronic system. Their design, modeling and optimization are becoming complex and computationally expensive with the evolution of semiconductor technology as the devices of nanometer dimensions are being used. In high-speed applications, system level simulations are needed to ensure the robustness of a system in terms of signal and power quality. The simulations are becoming very expensive because of the large dimensional systems and their full-wave models. Machine learning techniques can be used as computationally efficient alternatives in the design cycle of the interconnects. This paper presents a review of the applications of machine learning techniques for design, optimization and analysis of interconnects in high-speed electronic systems. A holistic discussion is presented, including the basics of interconnects, their impact on the system performance, popular machine learning techniques and their applications related to the interconnects. The performance evaluation, optimization and variability analysis of interconnects are discussed in detail. Future scope and overlook that are presented in the literature are also discussed.

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Demonstration of CMOS-Compatible Multi-Level Graphene Interconnects With Metal Vias

Cited by 11 publications

References 39 publications

Quantum‐Engineered Devices Based on 2D Materials for Next‐Generation Information Processing and Storage

Quantum‐Engineered Devices Based on 2D Materials for Next‐Generation Information Processing and Storage

Advances in multilayer graphene processes for metallization and high-frequency devices

Machine Learning Techniques for Modeling and Performance Analysis of Interconnects

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