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arxiv: 2409.03993 · v1 · submitted 2024-09-06 · ❄️ cond-mat.mes-hall · quant-ph

CMOS compatibility of semiconductor spin qubits

Pith reviewed 2026-05-23 21:11 UTC · model grok-4.3

classification ❄️ cond-mat.mes-hall quant-ph
keywords semiconductor spin qubitsCMOS compatibilityfault-tolerant quantum computingVLSI integrationsilicon quantum devicesquantum hardware scaling
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The pith

Semiconductor spin qubits can leverage existing CMOS fabrication infrastructure to reach the scale needed for fault-tolerant quantum computing.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how semiconductor spin qubits can be made compatible with CMOS technology used in classical chips. It argues that this compatibility gives spin qubits an edge in scaling up to the millions of qubits required for useful fault-tolerant quantum computing. By reviewing the overlap with VLSI principles, the authors highlight where spin qubit designs match industry practices and where adjustments are needed. This review aims to guide collaboration between qubit developers and semiconductor manufacturers toward industrial production of quantum processors.

Core claim

Semiconductor spin qubits possess unique advantages in CMOS compatibility that position them as leading candidates for large-scale fault-tolerant quantum computing. The compatibility spans from using silicon wafers as substrates to co-integrating qubits with control electronics, allowing extrapolation from current quantum processors to future systems that meet FTQC requirements through established semiconductor industry methods.

What carries the argument

CMOS compatibility, ranging from wafer substrate use to full co-integration with high-yield electronics for qubit control.

Load-bearing premise

The integration challenges already solved by the semiconductor industry for classical VLSI systems can be directly retrofitted to qubit systems without introducing new quantum-specific barriers at scale.

What would settle it

Demonstration that spin qubit coherence times or gate fidelities drop significantly when fabricated using standard advanced CMOS processes at industrial scales would challenge the compatibility claim.

read the original abstract

Several domains of society will be disrupted once millions of high-quality qubits can be brought together to perform fault-tolerant quantum computing (FTQC). All quantum computing hardware available today is many orders of magnitude removed from the requirements for FTQC. The intimidating challenges associated with integrating such complex systems have already been addressed by the semiconductor industry -hence many qubit makers have retrofitted their technology to be CMOS-compatible. This compatibility, however, can have varying degrees ranging from the mere ability to fabricate qubits using a silicon wafer as a substrate, all the way to the co-integration of qubits with high-yield, low-power advanced electronics to control these qubits. Extrapolating the evolution of quantum processors to future systems, semiconductor spin qubits have unique advantages in this respect, making them one of the most serious contenders for large-scale FTQC. In this review, we focus on the overlap between state-of-the-art semiconductor spin qubit systems and CMOS industry Very Large-Scale Integration (VLSI) principles. We identify the main differences in spin qubit operation, material, and system requirements compared to well-established CMOS industry practices. As key players in the field are looking to collaborate with CMOS industry partners, this review serves to accelerate R&D towards the industrial scale production of FTQC processors.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. This review argues that semiconductor spin qubits possess unique advantages for large-scale fault-tolerant quantum computing (FTQC) stemming from their compatibility with CMOS VLSI principles. It surveys the spectrum of compatibility (from silicon-substrate fabrication to co-integration with control electronics), contrasts spin-qubit materials, control, and yield requirements with established CMOS practices, and identifies overlaps to accelerate industrial collaboration.

Significance. If the mapping of overlaps and gaps is accurate, the review could usefully inform qubit-CMOS co-design efforts. However, the central claim that spin qubits are thereby 'one of the most serious contenders' for FTQC rests on an untested extrapolation that classical CMOS solutions can be retrofitted without introducing new quantum-specific barriers at scale; the manuscript supplies no quantitative scaling analysis or cited demonstrations of FTQC-relevant metrics under co-integrated control beyond small arrays.

major comments (2)
  1. [Abstract] Abstract: the assertion of 'unique advantages' that make spin qubits 'one of the most serious contenders for large-scale FTQC' is load-bearing yet unsupported by any comparative scaling analysis or cited data on fidelity/coherence under co-integrated CMOS at >1000-qubit scale.
  2. [Introduction / overlap analysis sections] The review identifies differences in materials, control, and yield but provides no quantitative assessment of whether these differences introduce quantum-specific limits (e.g., charge-noise coupling from integrated CMOS or crosstalk at high density) that cannot be solved by existing industry processes; this assumption underpins the 'serious contender' conclusion.
minor comments (2)
  1. Notation for compatibility levels (substrate-only vs. full co-integration) is introduced but not used consistently when citing specific device demonstrations.
  2. Several cited works on classical CMOS yield statistics lack page or section references, making it difficult to verify the claimed parallels.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. The comments correctly identify that our review is prospective and does not contain new quantitative scaling data at FTQC-relevant scales. We have revised the manuscript to qualify the central claims more precisely, to state the assumptions explicitly, and to highlight the absence of large-scale co-integrated demonstrations. These changes do not alter the review's scope or its utility for mapping CMOS-qubit overlaps.

read point-by-point responses
  1. Referee: [Abstract] Abstract: the assertion of 'unique advantages' that make spin qubits 'one of the most serious contenders for large-scale FTQC' is load-bearing yet unsupported by any comparative scaling analysis or cited data on fidelity/coherence under co-integrated CMOS at >1000-qubit scale.

    Authors: We agree that the original abstract language extrapolated beyond the data presented. The review surveys compatibility overlaps and differences but contains no scaling analysis or >1000-qubit co-integrated metrics, as none exist in the literature. We have revised the abstract to replace the phrase 'one of the most serious contenders for large-scale FTQC' with 'offer a promising route toward large-scale FTQC that merits industrial-scale investigation,' and we have added an explicit caveat that current demonstrations remain at small arrays. This revision aligns the abstract with the manuscript's actual content. revision: yes

  2. Referee: [Introduction / overlap analysis sections] The review identifies differences in materials, control, and yield but provides no quantitative assessment of whether these differences introduce quantum-specific limits (e.g., charge-noise coupling from integrated CMOS or crosstalk at high density) that cannot be solved by existing industry processes; this assumption underpins the 'serious contender' conclusion.

    Authors: The manuscript is a review whose stated goal is to map overlaps between spin-qubit requirements and CMOS VLSI practices to facilitate collaboration; it does not claim to perform quantitative limit analysis. We acknowledge that the text does not evaluate whether identified differences create insurmountable quantum-specific barriers. In the revised version we have inserted a dedicated paragraph in the introduction that (i) states the assumption that industry processes can address the differences, (ii) notes the current absence of experimental data on charge-noise coupling or high-density crosstalk under co-integration, and (iii) identifies these topics as open research questions. This addition makes the underlying assumption transparent without expanding the review into a quantitative study. revision: yes

Circularity Check

0 steps flagged

Review paper aggregates external sources with no internal derivation chain

full rationale

This is a review paper that surveys overlaps between spin-qubit systems and CMOS VLSI practices, identifies differences, and discusses collaboration opportunities. The abstract and provided text contain no equations, fitted parameters, predictions, or self-citations used as load-bearing premises for a new result. The central claim is an assessment of advantages drawn from external literature rather than a derivation that reduces to its own inputs. No circular steps are present.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This review paper does not introduce new free parameters, axioms, or invented entities. It relies on the existing body of literature on semiconductor spin qubits and CMOS technology.

pith-pipeline@v0.9.0 · 5792 in / 943 out tokens · 22495 ms · 2026-05-23T21:11:15.981406+00:00 · methodology

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Spin Qubit Leapfrogging: Dynamics of shuttling electrons on top of another

    cond-mat.mes-hall 2026-04 unverdicted novelty 7.0

    Mobile spin qubits in silicon can leapfrog over occupied dots by exploiting low valley splitting, enabling new connectivity routes and SWAP^γ entangling gates.

  2. Two-dimensional Si spin qubit arrays with multilevel interconnects

    quant-ph 2025-02 unverdicted novelty 7.0

    Demonstration of an extendable 2D silicon spin qubit array with multilevel interconnects enabling high-fidelity exchange-only qubits and defect-tolerant reconfigurability.

  3. Gate Stack Engineering for High-Mobility and Low-Noise SiMOS Quantum Devices

    cond-mat.mes-hall 2026-03 unverdicted novelty 4.0

    Raising the atomic-layer deposition temperature of Al2O3 and using HfO2 or poly-Si gates in SiMOS devices correlates with higher mobility and lower charge noise, yielding more stable quantum dots.

  4. Device/circuit simulations of silicon spin qubits based on a gate-all-around transistor

    cond-mat.mes-hall 2025-12 unverdicted novelty 4.0

    TCAD device simulations and SPICE circuit simulations indicate that spin-dependent charge distributions in a GAA transistor produce distinguishable currents that a conventional CMOS sense amplifier can detect when vol...

Reference graph

Works this paper leans on

206 extracted references · 206 canonical work pages · cited by 4 Pith papers · 2 internal anchors

  1. [1]

    Semiconductor spin qubit flavours In its simplest form, a semiconductor spin qubit is formed by the spin state of a single electron or hole which is confined in every dimension by a potential. The two main approaches to confine charge carriers are donor implantation (e.g., P in bulk Si) producing an atomic -like 3D isotropic confinement or trapping the ch...

  2. [2]

    Two-qubit gates in each spin qubit implementation are enabled by spin -exchange interactions between neighbouring quantum dots 60. Scalable architectures must be able to switch exchange interactions electrically between ON/OFF regimes by moving the electron wavefunctions ‘closer’ and ‘farther’ apart – either in real space or by virtue of additional electr...

  3. [3]

    In this section we highlight the applicability of current state -of-the-art CMOS technologies for semiconductor spin qubits

    Current qubit device compatibility with CMOS manufacturing Both semiconductor spin qubits and CMOS technology are umbrella categories, spanning a wide range of variation in device materials, designs, and applications. In this section we highlight the applicability of current state -of-the-art CMOS technologies for semiconductor spin qubits. We start with ...

  4. [4]

    In the original context of the microelectronics industry, the scaling laws of transistors referred to the increase in computational power at a fixed total resource budget

    Scaling challenges for full-stack CMOS QC A semantic variation of the term scaling is being observed over time. In the original context of the microelectronics industry, the scaling laws of transistors referred to the increase in computational power at a fixed total resource budget. For instance, a total chip area, total manufacturing cost or total power ...

  5. [5]

    With above challenges in direct testing, many of the efforts are devoted to indirect testing with faster turnaround to infer the qubit (or part of the qubit structure) performance

    Recent demonstration of on -chip multiplexing and switching matrix routers offer an alternative pathway towards high-volume qubit characterizaiton176,177. With above challenges in direct testing, many of the efforts are devoted to indirect testing with faster turnaround to infer the qubit (or part of the qubit structure) performance. Moreover, standard CM...

  6. [6]

    At room temperature, the device electrode and ohmic contact yield can readily be verified

    Industr y standard automatic probe stations have also been applied to qubit devices. At room temperature, the device electrode and ohmic contact yield can readily be verified. Furthermore, cryogenic automatic probe stations operating around 1 .6 K have been developed for 300mm wafers178,179. They can provide fast learning on some of the key qubit structur...

  7. [7]

    Conclusion A fault tolerant quantum computer has the potential to revolutionize several areas of our society. However, reaching that potential requires scaling hardware from a few tens to a few million high- quality qubits, based on available quantum error correction codes and quantum algorithms. Manufacturing such high numbers of devices has only been de...

  8. [8]

    Advanced BEOL and 2.5D/3D technology, such as backside power delivery, could help solve QPU interconnect and IO bottlenecks, if shown to be compatible with cryogenic operation and spin qubit control

  9. [9]

    Bogobowicz, M. et al. Quantum Technology Monitor. 54 https://www.mckinsey.com/~/media/mckinsey/business%20functions/mckinsey%20digi tal/our%20insights/quantum%20technology%20sees%20record%20investments%20pro gress%20on%20talent%20gap/quantum-technology-monitor-april-2023.pdf (2023)

  10. [10]

    Scholten, T. L. et al. Assessing the Benefits and Risks of Quantum Computers. Preprint at https://doi.org/10.48550/arXiv.2401.16317 (2024)

  11. [11]

    Fellous-Asiani, M. et al. Optimizing Resource Efficiencies for Scalable Full-Stack Quantum Computers. PRX Quantum 4, 040319 (2023)

  12. [12]

    General Relativity,

    Corrado, C., Haskel, J., Miranda, J. & Sichel, D. Measuring and Accounting for Innovation in the Twenty-First Century. (2021) doi:10.7208/chicago/9780226728209.001.0001

  13. [13]

    & Pryde, G

    Slussarenko, S. & Pryde, G. J. Photonic quantum information processing: A concise review. Appl. Phys. Rev. 6, 041303 (2019)

  14. [14]

    Engineering high-coherence superconducting qubits

    Siddiqi, I. Engineering high-coherence superconducting qubits. Nat. Rev. Mater. 6, 875– 891 (2021). 22

  15. [15]

    Romaszko, Z. D. et al. Engineering of microfabricated ion traps and integration of advanced on-chip features. Nat. Rev. Phys. 2, 285–299 (2020)

  16. [16]

    de Leon, N. P. et al. Materials challenges and opportunities for quantum computing hardware. Science 372, (2021)

  17. [17]

    Tyryshkin, A. M. et al. Coherence of spin qubits in silicon. J. Phys. Condens. Matter 18, S783 (2006)

  18. [18]

    Voisin, B. et al. Few-Electron Edge-State Quantum Dots in a Silicon Nanowire Field-Effect Transistor. Nano Lett. 14, 2094–2098 (2014)

  19. [19]

    Zwerver, A. M. J. et al. Qubits made by advanced semiconductor manufacturing. Nat. Electron. 2022 53 5, 184–190 (2022)

  20. [20]

    Powell, J. R. The Quantum Limit to Moore’s Law. Proc. IEEE 96, 1247–1248 (2008)

  21. [21]

    Elsayed, A. et al. Low charge noise quantum dots with industrial CMOS manufacturing

  22. [22]

    Camenzind, T. N. et al. High mobility SiMOSFETs fabricated in a full 300mm CMOS process. (2021)

  23. [23]

    Maurand, R. et al. A CMOS silicon spin qubit. Nat. Commun. 7, 13575 (2016)

  24. [24]

    Paraskevopoulos, N., Sebastiano, F., Almudever, C. G. & Feld, S. SpinQ: Compilation Strategies for Scalable Spin-Qubit Architectures. ACM Trans. Quantum Comput. 5, 4:1- 4:36 (2023)

  25. [25]

    https://www.businesswire.com/news/home/20230615304303/en/Intel%E2%80%99s- New-Chip-to-Advance-Silicon-Spin-Qubit-Research-for-Quantum-Computing (2023)

    Intel’s New Chip to Advance Silicon Spin Qubit Research for Quantum Computing. https://www.businesswire.com/news/home/20230615304303/en/Intel%E2%80%99s- New-Chip-to-Advance-Silicon-Spin-Qubit-Research-for-Quantum-Computing (2023)

  26. [26]

    Shor, P. W. Scheme for Reducing Decoherence in Quantum Computer Memory. Physical Review A vol. 52 (1995)

  27. [27]

    Krinner, S. et al. Realizing repeated quantum error correction in a distance-three surface code. Nature 605, 669–674 (2022)

  28. [28]

    Zhao, Y. et al. Realization of an Error-Correcting Surface Code with Superconducting Qubits. Phys. Rev. Lett. 129, 030501 (2022)

  29. [29]

    Acharya, R. et al. Suppressing quantum errors by scaling a surface code logical qubit. Nature 614, 676–681 (2023)

  30. [30]

    Chen, Z. et al. Exponential suppression of bit or phase flip errors with repetitive error correction. Nature 595, 383 (2021)

  31. [31]

    Xue, X. et al. Quantum logic with spin qubits crossing the surface code threshold. Nature 601, 343 (2022)

  32. [32]

    Noiri, A. et al. Fast universal quantum gate above the fault-tolerance threshold in silicon. 338 Nat. 601, (2022)

  33. [33]

    Tanttu, T. et al. Stability of high-fidelity two-qubit operations in silicon. Preprint at https://doi.org/10.48550/arXiv.2303.04090 (2023)

  34. [34]

    Huang, J. Y. et al. High-fidelity operation and algorithmic initialisation of spin qubits above one kelvin. Preprint at https://doi.org/10.48550/arXiv.2308.02111 (2023)

  35. [35]

    Mądzik, M. T. et al. Precision tomography of a three-qubit donor quantum processor in silicon. Nature 601, 348–353 (2022)

  36. [36]

    Babbush, R. et al. Focus beyond Quadratic Speedups for Error-Corrected Quantum Advantage. PRX Quantum 2, 010103 (2021)

  37. [37]

    G., Mariantoni, M., Martinis, J

    Fowler, A. G., Mariantoni, M., Martinis, J. M. & Cleland, A. N. Surface codes: Towards practical large-scale quantum computation. Phys. Rev. - At. Mol. Opt. Phys. 86, (2012)

  38. [38]

    & Pachos, J

    Lahtinen, V. & Pachos, J. A Short Introduction to Topological Quantum Computation. SciPost Phys. 3, 021 (2017)

  39. [39]

    Terhal, B. M. Quantum error correction for quantum memories. Rev. Mod. Phys. 87, 307– 346 (2015)

  40. [40]

    Ladd, T. D. et al. Quantum computers. Nature 464, 45–53 (2010)

  41. [41]

    Quest for qubits

    Popkin, G. Quest for qubits. Science 354, 1090–1093 (2016). 23

  42. [42]

    Weste, N. H. E. & Harris, D. CMOS VLSI Design : A Circuits and Systems Perspective. (Pearson India, 2015)

  43. [43]

    Zhang, K. et al. Modular quantum computation in a trapped ion system. Nat. Commun. 10, 4692 (2019)

  44. [44]

    https://pubs.aip.org/aip/jap/article/132/16/160902/2837574/The-future-of-quantum- computing-with

    The future of quantum computing with superconducting qubits | Journal of Applied Physics | AIP Publishing. https://pubs.aip.org/aip/jap/article/132/16/160902/2837574/The-future-of-quantum- computing-with

  45. [45]

    Bartolucci, S. et al. Fusion-based quantum computation. Nat. Commun. 14, 912 (2023)

  46. [46]

    Das, P. et al. AFS: Accurate, Fast, and Scalable Error-Decoding for Fault-Tolerant Quantum Computers. in 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA) 259–273 (2022). doi:10.1109/HPCA53966.2022.00027

  47. [47]

    Beverland, M. E. et al. Assessing requirements to scale to practical quantum advantage. Preprint at https://doi.org/10.48550/arXiv.2211.07629 (2022)

  48. [48]

    P., Clarke, J

    Franke, D. P., Clarke, J. S., Vandersypen, L. M. K. & Veldhorst, M. Rent’s rule and extensibility in quantum computing. Microprocess. Microsyst. 67, 1–7 (2019)

  49. [49]

    Li, R. et al. A crossbar network for silicon quantum dot qubits. Sci. Adv. 4, (2018)

  50. [50]

    Borsoi, F. et al. Shared control of a 16 semiconductor quantum dot crossbar array. Preprint at https://doi.org/10.48550/arXiv.2209.06609 (2022)

  51. [51]

    Asenov, A. et al. Simulation of statistical variability in nano-CMOS transistors using drift- diffusion, Monte Carlo and non-equilibrium Green’s function techniques. J. Comput. Electron. 8, 349–373 (2009)

  52. [52]

    Neyens, S. et al. Probing single electrons across 300 mm spin qubit wafers. Preprint at https://doi.org/10.48550/arXiv.2307.04812 (2023)

  53. [53]

    https://ieeexplore.ieee.org/document/9099264

    Variability and Fidelity Limits of Silicon Quantum Gates Due to Random Interface Charge Traps. https://ieeexplore.ieee.org/document/9099264

  54. [54]

    & Niquet, Y.-M

    Martinez, B. & Niquet, Y.-M. Variability of Electron and Hole Spin Qubits Due to Interface Roughness and Charge Traps. Phys. Rev. Appl. 17, 024022 (2022)

  55. [55]

    Sabbagh, D. et al. Quantum Transport Properties of Industrial Si 28 / Si O2 28. Phys. Rev. Appl. 12, 1–5 (2019)

  56. [56]

    Hansen, I. et al. Implementation of the SMART protocol for global qubit control in silicon

  57. [57]

    Hansen, I. et al. The SMART protocol -- Pulse engineering of a global field for robust and universal quantum computation. (2021) doi:10.1103/PhysRevA.104.062415

  58. [58]

    Camenzind, L. C. et al. A hole spin qubit in a fin field-effect transistor above 4 kelvin. Nat. Electron. 5, 178–183 (2022)

  59. [59]

    Yang, C. H. et al. Operation of a silicon quantum processor unit cell above one kelvin. Nature 580, 350–354 (2020)

  60. [60]

    Petit, L. et al. High-Fidelity Two-Qubit Gates in Silicon above One Kelvin. arXiv http://arxiv.org/abs/2007.09034 (2020)

  61. [61]

    Cifuentes, J. D. et al. Bounds to electron spin qubit variability for scalable CMOS architectures. Preprint at https://doi.org/10.48550/arXiv.2303.14864 (2023)

  62. [62]

    Rimbach-Russ, M., Philips, S. G. J., Xue, X. & Vandersypen, L. M. K. Simple framework for systematic high-fidelity gate operations. Preprint at https://doi.org/10.48550/arXiv.2211.16241 (2022)

  63. [63]

    Watson, T. F. et al. A programmable two-qubit quantum processor in silicon. Nature 555, 633–637 (2018)

  64. [64]

    D., Pan, A., Nichol, J

    Burkard, G., Ladd, T. D., Pan, A., Nichol, J. M. & Petta, J. R. Semiconductor spin qubits. Rev. Mod. Phys. 95, 025003 (2023)

  65. [65]

    & Devoret, M

    Bouchiat, V., Vion, D., Joyez, P., Esteve, D. & Devoret, M. H. Quantum coherence with a single Cooper pair. Phys. Scr. 1998, 165 (1998). 24

  66. [66]

    Nakamura, Y., Pashkin, Yu. A. & Tsai, J. S. Coherent control of macroscopic quantum states in a single-Cooper-pair box. Nature 398, 786–788 (1999)

  67. [67]

    Weinstein, A. J. et al. Universal logic with encoded spin qubits in silicon. Nature 615, 817– 822 (2023)

  68. [68]

    Veldhorst, M. et al. A two-qubit logic gate in silicon. Nature 526, 410–414 (2015)

  69. [69]

    Vandersypen, L. M. K. et al. Interfacing spin qubits in quantum dots and donors— hot, dense, and coherent. Npj Quantum Inf. 3, 1–10 (2017)

  70. [70]

    Veldhorst, M., Eenink, H. G. J., Yang, C. H. & Dzurak, A. S. Silicon CMOS architecture for a spin-based quantum computer. Nat. Commun. 8, (2017)

  71. [71]

    Bonen, S. et al. Cryogenic Characterization of 22-nm FDSOI CMOS Technology for Quantum Computing ICs. IEEE Electron Device Lett. 40, 127–130 (2019)

  72. [72]

    Guevel, L. L. et al. A 110mK 295μW 28nm FDSOI CMOS Quantum Integrated Circuit with a 2.8GHz Excitation and nA Current Sensing of an On-Chip Double Quantum Dot. Dig. Tech. Pap. - IEEE Int. Solid-State Circuits Conf. 2020-February, 306–308 (2020)

  73. [73]

    Tripathi, S. P. Cryogenic Characterization and modelling of FinFET technology. (2022)

  74. [74]

    Han, H.-C. et al. Cryogenic Characterization of 16 nm FinFET Technology for Quantum Computing. in ESSCIRC 2021 - IEEE 47th European Solid State Circuits Conference (ESSCIRC) 71–74 (2021). doi:10.1109/ESSCIRC53450.2021.9567747

  75. [75]

    Pati Tripathi, S. et al. Characterization and Modelling of Quantum Dot Behaviour in FDSOI Devices. doi:10.1109/JEDS.2022.3176205

  76. [76]

    Cao, W. et al. The future transistors. Nature 620, 501–515 (2023)

  77. [77]

    GlobalFoundries https://gf.com/technology-platforms/sige/

    SiGe. GlobalFoundries https://gf.com/technology-platforms/sige/

  78. [78]

    Planes, N. et al. 28nm FDSOI technology platform for high-speed low-voltage digital applications. in 2012 Symposium on VLSI Technology (VLSIT) 133–134 (2012). doi:10.1109/VLSIT.2012.6242497

  79. [79]

    Carter, R. et al. 22nm FDSOI technology for emerging mobile, Internet-of-Things, and RF applications. in 2016 IEEE International Electron Devices Meeting (IEDM) 2.2.1-2.2.4 (2016). doi:10.1109/IEDM.2016.7838029

  80. [80]

    Min, D. et al. 18nm FDSOI Technology Platform embedding PCM & Innovative Continuous- Active Construct Enhancing Performance for Leading-Edge MCU Applications. in 2021 IEEE International Electron Devices Meeting (IEDM) 13.1.1-13.1.4 (2021). doi:10.1109/IEDM19574.2021.9720542

Showing first 80 references.