Relevant bibliographies by topics / Topological Computing

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Topological Computing'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Topological Computing"

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CHEON, Sangmo. "In Search of Majorana Fermions: Topological Superconductors and Quantum Computing." Physics and High Technology 34, no. 5 (2025): 17–25. https://doi.org/10.3938/phit.34.015.

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The combination of topology and superconductivity has led to the discovery of topological superconductors—quantum phases that host Majorana zero modes (MZMs), exotic quasiparticles with non-Abelian statistics. These modes offer a pathway to fault-tolerant quantum computation, in which quantum information is stored nonlocally and manipulated via topologically protected operations. We review the theoretical foundations of Majorana fermions, the Kitaev chain, and the mathematical structure of topological superconductors. We highlight their potential realization in semiconductor nanowires and also
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Scappucci, G., P. J. Taylor, J. R. Williams, T. Ginley, and S. Law. "Crystalline materials for quantum computing: Semiconductor heterostructures and topological insulators exemplars." MRS Bulletin 46, no. 7 (2021): 596–606. http://dx.doi.org/10.1557/s43577-021-00147-8.

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AbstractHigh-purity crystalline solid-state materials play an essential role in various technologies for quantum information processing, from qubits based on spins to topological states. New and improved crystalline materials emerge each year and continue to drive new results in experimental quantum science. This article summarizes the opportunities for a selected class of crystalline materials for qubit technologies based on spins and topological states and the challenges associated with their fabrication. We start by describing semiconductor heterostructures for spin qubits in gate-defined q
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LEE, Hyun-Jung, and Mahn-Soo CHOI. "Applications of Topological Insulators and Topological Quantum Computing." Physics and High Technology 20, no. 3 (2011): 30. http://dx.doi.org/10.3938/phit.20.011.

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Vidya S Umadi. "Computing Topological Indices of Certain Networks." Communications on Applied Nonlinear Analysis 31, no. 2 (2024): 416–29. http://dx.doi.org/10.52783/cana.v31.611.

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In mathematical chemistry, topological indices are molecular descriptors that are calculated on the molecular graph of a chemical compound. The molecular graph is a graph which is obtained from some chemical structures. The degree of every molecular graph cannot exceeds 4. Topological indices are numerical quantities of a graph that describe its topology. An atom represents a vertex and a bond between two atoms represents an edge in a molecular graph. Mainly there are three types of topological indices viz., degree-based, distance based and eigenvalue-based topological indices. The first degre
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Fowler, A. G., and K. Goyal. "Topological cluster state quantum computing." Quantum Information and Computation 9, no. 9&10 (2009): 721–38. http://dx.doi.org/10.26421/qic9.9-10-1.

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The quantum computing scheme described by Raussendorf et. al (2007), when viewed as a cluster state computation, features a 3-D cluster state, novel adjustable strength error correction capable of correcting general errors through the correction of Z errors only, a threshold error rate approaching 1% and low overhead arbitrarily long-range logical gates. In this work, we review the scheme in detail framing the discussion solely in terms of the required 3-D cluster state and its stabilizers.
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Rowell, Eric C., and Zhenghan Wang. "Mathematics of topological quantum computing." Bulletin of the American Mathematical Society 55, no. 2 (2018): 183–238. http://dx.doi.org/10.1090/bull/1605.

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Palacio-Morales, Alexandra, Eric Mascot, Sagen Cocklin, et al. "Atomic-scale interface engineering of Majorana edge modes in a 2D magnet-superconductor hybrid system." Science Advances 5, no. 7 (2019): eaav6600. http://dx.doi.org/10.1126/sciadv.aav6600.

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Topological superconductors are predicted to harbor exotic boundary states—Majorana zero-energy modes—whose non-Abelian braiding statistics present a new paradigm for the realization of topological quantum computing. Using low-temperature scanning tunneling spectroscopy, here, we report on the direct real-space visualization of chiral Majorana edge states in a monolayer topological superconductor, a prototypical magnet-superconductor hybrid system composed of nanoscale Fe islands of monoatomic height on a Re(0001)-O(2 × 1) surface. In particular, we demonstrate that interface engineering by an
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Wang, Michelle, Cooper Doyle, Bryn Bell, et al. "Topologically protected entangled photonic states." Nanophotonics 8, no. 8 (2019): 1327–35. http://dx.doi.org/10.1515/nanoph-2019-0058.

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AbstractEntangled multiphoton states lie at the heart of quantum information, computing, and communications. In recent years, topology has risen as a new avenue to robustly transport quantum states in the presence of fabrication defects, disorder, and other noise sources. Whereas topological protection of single photons and correlated photons has been recently demonstrated experimentally, the observation of topologically protected entangled states has thus far remained elusive. Here, we experimentally demonstrate the topological protection of spatially entangled biphoton states. We observe rob
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Banerjee, Sourav. "Tuneable phononic crystals and topological acoustics." Open Access Government 42, no. 1 (2024): 252–53. http://dx.doi.org/10.56367/oag-042-11436.

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Tuneable phononic crystals and topological acoustics Sourav Banerjee, Professor from the University of South Carolina, navigates the field of tuneable phononic crystals and topological acoustics. Acoustics, an age-old field of study, has recently revealed new physics with new degrees of freedom of wave propagation. These new findings are invaluable for information processing using acoustic modality. Information processing using acoustics is called acoustic computing. Computing Boolean algebra, which has already been demonstrated, could pave the pathways even for quantum computing using acousti
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Jahn, M. W., and P. E. Bradley. "COMPUTING WATERTIGHT VOLUMETRIC MODELS FROM BOUNDARY REPRESENTATIONS TO ENSURE CONSISTENT TOPOLOGICAL OPERATIONS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences VIII-4/W2-2021 (October 7, 2021): 21–28. http://dx.doi.org/10.5194/isprs-annals-viii-4-w2-2021-21-2021.

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Abstract. To simulate environmental processes, noise, flooding in cities as well as the behaviour of buildings and infrastructure, ‘watertight’ volumetric models are a measuring prerequisite. They ensure topologically consistent 3D models and allow the definition of proper topological operations. However, in many existing city or other geo-information models, topologically unchecked boundary representations are used to store spatial entities. In order to obtain consistent topological models, including their ‘fillings’, in this paper, a triangulation combined with overlay and path-finding metho
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Dissertations / Theses on the topic "Topological Computing"

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Evans, Julia. "The algebra of topological quantum computing." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=107687.

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Topological quantum computing is an approach to the problem of implementingquantum gates accurately and robustly. The idea is to exploit topological propertiesof certain quasiparticles called anyons to obtain a proposed implementation of quan-tum computing which is inherently fault-tolerant. The mathematical structure thatdescribes anyons is that of modular tensor categories. These modular tensor cate-gories can be constructed from the representations of certain algebraic objects calledquantum groups. In this thesis we give an explanation of modular tensor categoriesand quantum groups as they
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Shi, Dayu. "Computing Topological Features for Data Analysis." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1512079255367232.

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Fan, Fengtao. "Computing Topological Features of Data and Shapes." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1385999908.

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Busaryev, Oleksiy. "On Computing and Tracking Geometrical and Topological Features." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1354679582.

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Muhamed, Abera Ayalew. "Moduli spaces of topological solitons." Thesis, University of Kent, 2015. https://kar.kent.ac.uk/47961/.

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This thesis presents a detailed study of phenomena related to topological solitons (in $2$-dimensions). Topological solitons are smooth, localised, finite energy solutions in non-linear field theories. The problems are about the moduli spaces of lumps in the projective plane and vortices on compact Riemann surfaces. Harmonic maps that minimize the Dirichlet energy in their homotopy classes are known as lumps. Lump solutions in real projective space are explicitly given by rational maps subject to a certain symmetry requirement. This has consequences for the behaviour of lumps and their symmetr
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Litinski, Daniel [Verfasser]. "Large-Scale Topological Quantum Computing with and without Majorana Fermions / Daniel Litinski." Berlin : Freie Universität Berlin, 2019. http://d-nb.info/1193995310/34.

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Sens, Aaron M. "Topology Preserving Data Reductions for Computing Persistent Homology." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627658247850018.

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Santos, Francisco C. "Topological evolution: from biological to social networks." Doctoral thesis, Universite Libre de Bruxelles, 2007. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210702.

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Malott, Nicholas O. "Partitioned Persistent Homology." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613748601295702.

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Lumia, Luca. "Digital quantum simulations of Yang-Mills lattice gauge theories." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22355/.

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I metodi di calcolo tradizionali per le teorie di gauge su reticolo risultano problematici in regioni di diagrammi di fase a grandi valori del potenziale chimico o quando sono utilizzate per riprodurre la dinamica in tempo reale di un modello. Tali problemi possono essere evitati da simulazioni quantistiche delle teorie di gauge su reticolo, le quali stanno diventando sempre più riproducibili sperimentalmente, grazie ai recenti progressi tecnologici. In questa tesi formuliamo una versione delle teorie di Yang-Mills su reticolo appropriata per risolvere il problema della dimensione infinita del
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Books on the topic "Topological Computing"

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Workshop on Topology Based Methods in Data Analysis and Visualization (4th : 2011 : Zürich, Switzerland), ed. Topological methods in data analysis and visualization II: Theory, algorithms, and applications. Springer, 2012.

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Reyes, Mauricio, Pedro Henriques Abreu, Jaime Cardoso, et al., eds. Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87444-5.

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Quantum Computation with Topological Codes: From Qubit to Topological Fault-Tolerance. Springer, 2015.

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Lexicographical manipulations for correctly computing regular tetrahedralizations with incremental topological flipping. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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Introduction to Topological Quantum Matter and Quantum Computation. Taylor & Francis Group, 2016.

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Introduction to Topological Quantum Matter and Quantum Computation. Taylor & Francis Group, 2024.

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Introduction to Topological Quantum Matter and Quantum Computation. Taylor & Francis Group, 2024.

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Stanescu, Tudor D. INTRODUCTION to TOPOLOGICAL QUANTUM MATTER and QUANTUM COMPUTATION. Taylor & Francis Group, 2020.

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Stanescu, Tudor D. Introduction to Topological Quantum Matter and Quantum Computation. Taylor & Francis Group, 2016.

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Stanescu, Tudor D. Introduction to Topological Quantum Matter and Quantum Computation. Taylor & Francis Group, 2016.

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Book chapters on the topic "Topological Computing"

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LaPierre, Ray. "Topological Quantum Computing." In The Materials Research Society Series. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69318-3_26.

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Easttom, Chuck. "Topological Quantum Computing." In Hardware for Quantum Computing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-66477-9_10.

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Walter Ogburn, R., and John Preskill. "Topological Quantum Computation." In Quantum Computing and Quantum Communications. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-49208-9_31.

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Kouzaev, Guennadi A. "EM Topological Signaling and Computing." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30310-4_10.

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Acharjee, Santanu, Amlanjyoti Oza, and Upashana Gogoi. "Topological Aspects of Granular Computing." In Advances in Topology and Their Interdisciplinary Applications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0151-7_12.

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Ganguly, Srinjoy, Shalini Devendrababu, Hasan Mustafa, Prateek Jain, and Luis Gerardo Ayala Bertel. "Topological Quantum Computing for Engineers." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-73477-9_12.

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Lockett, Alan J. "Search and Optimization in Topological Spaces." In Natural Computing Series. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62007-6_5.

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Ping, Chen, Zhengyan Zhang, Chen Dingfang, Hu Jiquan, Bin Shan, and Li Bo. "Topological Reconstruction Based on STL Model." In Human Centered Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15554-8_58.

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Rao, K. Venkateswara, A. Govardhan, and K. V. Chalapati Rao. "Discovering Spatiotemporal Topological Relationships." In Advances in Computing and Information Technology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22555-0_52.

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Bagchi, Susmit. "Distributed Computing in Monotone Topological Spaces." In Communications in Computer and Information Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34099-9_23.

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Conference papers on the topic "Topological Computing"

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Flower, Christopher J., Mahmoud Jalali-Mehrabad, Lida Xu, et al. "Observation of topological frequency combs." In Quantum Computing, Communication, and Simulation V, edited by Philip R. Hemmer and Alan L. Migdall. SPIE, 2025. https://doi.org/10.1117/12.3043985.

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Xu, Yao, and Gene Cooperman. "Enabling Practical Transparent Checkpointing for MPI: A Topological Sort Approach." In 2024 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2024. http://dx.doi.org/10.1109/cluster59578.2024.00028.

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Chiu, Cheng-Hsiang, Chedi Morchdi, Yi Zhou, Boyang Zhang, Che Chang, and Tsung-Wei Huang. "Reinforcement Learning-Generated Topological Order for Dynamic Task Graph Scheduling." In 2024 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2024. https://doi.org/10.1109/hpec62836.2024.10938506.

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Iyer, Vijeta, K. Meena, and D. Arivuoli. "Study on Special Bipartite Graph through Topological Framework." In 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). IEEE, 2025. https://doi.org/10.1109/icaeca63854.2025.11012620.

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Kauffman, Louis H., and Samuel J. Lomonaco. "Extended topological quantum computing." In SPIE Defense, Security, and Sensing, edited by Eric Donkor, Andrew R. Pirich, and Howard E. Brandt. SPIE, 2013. http://dx.doi.org/10.1117/12.2015001.

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Wan, Kai, Mingyue Ji, and Giuseppe Caire. "Topological Coded Distributed Computing." In GLOBECOM 2020 - 2020 IEEE Global Communications Conference. IEEE, 2020. http://dx.doi.org/10.1109/globecom42002.2020.9322245.

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Jabi, Wassim, Robert Aish, Simon Lannon, Aikaterini Chatzivasileiadi, and Nicholas Mario Wardhana. "Topologic - A toolkit for spatial and topological modelling." In eCAADe 2018: Computing for a better tomorrow. eCAADe, 2018. http://dx.doi.org/10.52842/conf.ecaade.2018.2.449.

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Dey, Tamal K., Fengtao Fan, and Yusu Wang. "Computing Topological Persistence for Simplicial Maps." In Annual Symposium. ACM Press, 2014. http://dx.doi.org/10.1145/2582112.2582165.

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OOTSUKA, TAKAYOSHI, and KAZUHIRO SAKUMA. "BRAID GROUP AND TOPOLOGICAL QUANTUM COMPUTING." In Summer School on Mathematical Aspects of Quantum Computing. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812814487_0002.

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Kauffman, Louis H., and Samuel J. Lomonaco, Jr. "Spin networks and anyonic topological computing." In Defense and Security Symposium, edited by Eric J. Donkor, Andrew R. Pirich, and Howard E. Brandt. SPIE, 2006. http://dx.doi.org/10.1117/12.666291.

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Reports on the topic "Topological Computing"

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Pasupuleti, Murali Krishna. Quantum Semiconductors for Scalable and Fault-Tolerant Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rr825.

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Abstract: Quantum semiconductors are revolutionizing computing by enabling scalable, fault-tolerant quantum processors that overcome the limitations of classical computing. As quantum technologies advance, superconducting qubits, silicon spin qubits, topological qubits, and hybrid quantum-classical architectures are emerging as key solutions for achieving high-fidelity quantum operations and long-term coherence. This research explores the materials, device engineering, and fabrication challenges associated with quantum semiconductors, focusing on quantum error correction, cryogenic control sys
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Bernal, J. Lexicographical manipulations for correctly computing regular tetrahedralizations with incremental topological flipping. National Institute of Standards and Technology, 1999. http://dx.doi.org/10.6028/nist.ir.6335.

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Guha, Supratik, H. S. Philip Wong, Jean Anne Incorvia, and Srabanti Chowdhury. Future Directions Workshop: Materials, Processes, and R&D Challenges in Microelectronics. Defense Technical Information Center, 2022. http://dx.doi.org/10.21236/ad1188476.

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Microelectronics is a complex field with ever-evolving technologies and business needs, fueled by decades of continued fundamental materials science and engineering advancement. Decades of dimensional scaling have led to the point where even the name microelectronics inadequately describes the field, as most modern devices operate on the nanometer scale. As we reach physical limits and seek more efficient ways for computing, research in new materials may offer alternative design approaches that involve much more than electron transport e.g. photonics, spintronics, topological materials, and a
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