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Record W7018226052

Cryogenic CMOS Compact Modeling for Cryo-Electronic Applications

2023· dissertation· en· W7018226052 on OpenAlexfundno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundUniversity of Waterloo
KeywordsQuantum computerQubitQuantum technologyQuantum circuitQuantum annealingQuantumQuantum sensorQuantum networkCoherence (philosophical gambling strategy)
DOInot available

Abstract

fetched live from OpenAlex

Quantum computing holds the promise of a monumental leap in computational power, enabling the resolution of previously insurmountable problems with astonishing speed compared to classical computers. \nEmerging computing paradigms, including Shor's factoring algorithm, Grover's searching algorithm, quantum simulations, protein folding, and more, stand on the brink of feasibility, thanks to quantum computers. \nHowever, despite impressive recent advancements in quantum computing, demonstrated systems featuring anywhere between a few to around a hundred physical qubits remain significantly distant from achieving quantum supremacy over classical computing, which demands the utilization of millions of physical qubits. \nThis formidable challenge is known as the scaling problem. \n \nImplementing large-scale quantum computing systems is faced with numerous hurdles, particularly \nwhere each qubit necessitates precise control under extremely low cryogenic temperatures (<1 K). \nComplementary metal-oxide-semiconductor (CMOS) technology, the cornerstone of classical computer scaling, emerges as a promising solution for scaling quantum computers. \nCMOS technology offers deep miniaturization and versatility, functioning seamlessly at both room temperature (RT) and cryogenic temperatures (cryoT). \nCMOS is compatible with the spin qubits in semiconductor quantum dots (one of the various methods of implementing qubits that exhibit long coherence time) offering integration compatibility especially from the fabrication perspective. \nIt is this kind of tight integration that may ultimately hold the key to resolving the quantum scaling problem, bridging the gap between the current state of quantum computing and its promising potential. \n \nNevertheless, current circuit design environments lack support for operating temperatures \nnear cryoT. \nThis lack of support is centered in the often overlooked component known as the compact model. \nCompact models act as the blueprint that informs circuit simulators of how circuit elements behave under various operating conditions. \nThis component is composed of simplified mathematical formulas that bridge the gap between the element's physical model and simulation engines. \nIn order to obtain accurate simulation results necessary for cryo-circuits design the compact model must be accurate. \nTo obtain precise simulation results necessary for cryo-circuit design, it is imperative to understand and incorporate the effects of cryoT on metal-oxide-semiconductor field-effect-transistors (MOSFETs) into the compact model. \n \nThis thesis is one of the first attempts to develop cryogenic MOSFET compact model based on virtual source concept, through theoretical investigation and experimental validation. \nThe cold temperature effects on MOSFETs are studied and integrated into the existing MIT virtual-source model (MVS), expanding its temperature range to include deep cryoT in the range of few Kelvin. \nTo achieve this, sample devices from multiple commercial technology nodes are characterized in RT down to deep cryoT. \nThe thesis outlines the measurement setup and explores a range of predicted and unexpected cryogenic phenomena within the transistor. \nGood agreement between experimental data and modeled data is obtained between 300 K and 4 K for 20, 28, and 65 nm bulk CMOS technology nodes. \n \nHowever, merely developing a cryogenic compact model is insufficient for its adoption and practical deployment. \nExtraction and fitting tools are therefore developed alongside the model. \nTo support circuit design on industrial tools and validate the model, the compact model is implemented Verilog-A. \nSubsequently, a cryogenic circuit simulation is demonstrated using industry-standard electronic design automation (EDA) tools. \nThis demonstration underscores the viability of the model to facilitate cryo-circuit design for quantum computing, representing a significant step towards realizing the potential of quantum computing in practical applications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.218
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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