Analysis of BTI in 300 mm integrated dual-gate WS2 FETs

Panarella, L.; Smets, Quentin; Verreck, Devin; Schram, T.; Cott, Daire; Asselberghs, Inge; Kaczer, B.

doi:10.1109/drc55272.2022.9855819

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Additionally, since this approach is based on color difference, it has the potential to extend beyond graphene and enable the differentiation of other 2D materials as well. Recently, 2D material-based electronic devices have been practically incorporated into the integrated CMOS system [ 35 , 36 , 37 ]. To adopt 2D materials into the fabrication process of integrated circuits, a fast and cost-effective method for distinguishing a single layer of 2D materials is required.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Assisted Identification of Single-Layer Graphene via Color Variation Analysis

Yang,

Seo,

Rhee

et al. 2024

Nanomaterials

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Techniques such as using an optical microscope and Raman spectroscopy are common methods for detecting single-layer graphene. Instead of relying on these laborious and expensive methods, we suggest a novel approach inspired by skilled human researchers who can detect single-layer graphene by simply observing color differences between graphene flakes and the background substrate in optical microscope images. This approach implemented the human cognitive process by emulating it through our data extraction process and machine learning algorithm. We obtained approximately 300,000 pixel-level color difference data from 140 graphene flakes from 45 optical microscope images. We utilized the average and standard deviation of the color difference data for each flake for machine learning. As a result, we achieved F1-Scores of over 0.90 and 0.92 in identifying 60 and 50 flakes from green and pink substrate images, respectively. Our machine learning-assisted computing system offers a cost-effective and universal solution for detecting the number of graphene layers in diverse experimental environments, saving both time and resources. We anticipate that this approach can be extended to classify the properties of other 2D materials.

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Section: Discussionmentioning

confidence: 99%

Machine Learning-Assisted Identification of Single-Layer Graphene via Color Variation Analysis

Yang,

Seo,

Rhee

et al. 2024

Nanomaterials

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“…Firstly, the weak out-of-plane Van der Waals (VdW) interactions exerted by 2D semiconductors hinder the nucleation of oxide materials and are responsible for the formation of non-passivated bond-rich interfaces and defect-rich gate dielectrics, which in turn result in large charge trapping and limit the use of typical 3D insulators. [7][8][9][10][11][12] Secondly, the fabrication of good metal/2D semiconductor contacts has proven to be anything but trivial due to untunable Schottky barrier heights and strong Fermi level pinning, which eventually cause excessively large contact resistances. [13][14][15] In addition to that, the choice of contact geometry for fully integrated devices is still a matter of debate as two major options are currently under investigation: 16,17 top contacts, i.e.…”

Section: Introductionmentioning

confidence: 99%

Direct Evidence of Contact-Induced Variability in Industrially-Fabricated Highly-Scaled MoS2 FETs

Panarella,

Kaczer,

Smets

et al. 2024

Preprint

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Evidence of microscopic inhomogeneities of the side source/drain contacts in 300 mm wafer integrated MoS2 FETs is presented. In particular, the presence of a limited number of low Schottky barrier spots through which channel carriers are predominantly injected is demonstrated by the dramatic current changes induced by individual charge traps located near the source contact. Two distinct types of "contact-impacting traps" are identified. Type-1 trap is adjacent to the contact interface and exchanges carriers with the metal. Its impact is only observable when the adjacent contact is the reverse-biased FET source and limits the channel current. Type-2 trap is located in the AlOx interlayer, near the source contact, and exchanges carriers with the channel. Its capture/emission time constants exhibit both a gate and drain bias dependence due to the high sensitivity of the contact regions to the applied lateral and vertical fields. Unlike typical channel-impacting oxide traps, both types of reported defects affect the Schottky barrier height rather than the threshold voltage and result in giant random telegraph noise (RTN). These observations indicate that the contact quality and geometry play a fundamental role in the ultimate scaling of 2D FETs.

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Evidence of contact-induced variability in industrially-fabricated highly-scaled MoS2 FETs

Panarella,

Kaczer,

Smets

et al. 2024

npj 2D Mater Appl

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Evidence of microscopic inhomogeneities of the side source/drain contacts in 300 mm wafer integrated MoS2 field-effect transistors is presented. In particular, the presence of a limited number of low Schottky barrier spots through which channel carriers are predominantly injected is demonstrated by the dramatic current changes induced by individual charge traps located near the source contact. Two distinct types of “contact-impacting traps” are identified. Type-1 trap is adjacent to the contact interface and exchanges carriers with the metal. Its impact is only observable when the adjacent contact is the reverse-biased FET source and limits the channel current. Type-2 trap is located in the AlOx gate oxide interlayer, near the source contact, and exchanges carriers with the channel. Its capture/emission time constants exhibit both a gate and drain bias dependence due to the high sensitivity of the contact regions to the applied lateral and vertical fields. Unlike typical channel-impacting oxide traps, both types of reported defects affect the Schottky barrier height and width rather than the threshold voltage and result in giant random telegraph noise (RTN). These observations indicate that the contact quality and geometry play a fundamental role in the ultimate scaling of 2D FETs.

show abstract

Analysis of BTI in 300 mm integrated dual-gate WS2 FETs

Cited by 4 publications

References 9 publications

Machine Learning-Assisted Identification of Single-Layer Graphene via Color Variation Analysis

Machine Learning-Assisted Identification of Single-Layer Graphene via Color Variation Analysis

Direct Evidence of Contact-Induced Variability in Industrially-Fabricated Highly-Scaled MoS2 FETs

Evidence of contact-induced variability in industrially-fabricated highly-scaled MoS2 FETs

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