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Cited by 372

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Clocking of molecular quantum-dot cellular automata

Hennessy, Kevin, Lent, Craig S.·2001·Journal of Vacuum Science & Technology B: Microelectronics and Nanometer Structures Processing, Measurement, and Phenomena

Abstract

Quantum-dot cellular automata (QCA) may provide a novel way to bypass the transistor paradigm to form ultrasmall computing elements. In the QCA paradigm information is represented in the charge configuration of a QCA cell, which maps naturally to a binary model. Molecular QCA implementations are being explored where the quantum dots in the cell take the form of redox centers in a molecule. Clocking has proved important in QCA cells synthesized so far. Here we examine a method to clock molecular QCA cells which are assembled at an interface. The clocking signals in this scheme originate from wires buried below the QCA surface. We present a simplified model of this clocking method and examine its behavior as a molecular shift register.

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Metadata

Authors
Hennessy, K., & Lent, C. S.
Venue
Journal of Vacuum Science & Technology B: Microelectronics and Nanometer Structures Processing, Measurement, and Phenomena
Published
2001-09-01
Volume
19
Issue
5
Pages
1752-1755
ISSN
  • 1071-1023 (ISSN-L)
  • 1520-8567
OpenAlex
W2051320857
Cited by
372
References
2
FWCI
11.69
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Clocking of molecular quantum-dot cellular automata. https://app.cochat.ai/papers/10.1116/1.1394729
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Hennessy, K., & Lent, C. S. (2001). Clocking of molecular quantum-dot cellular automata. Journal of Vacuum Science & Technology B: Microelectronics and Nanometer Structures Processing, Measurement, and Phenomena. https://doi.org/10.1116/1.1394729

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.