Multi-band machine-learning framework for reliable 5–30 GHz LC-DCO synthesis in 22-nm FDSOI.
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| Title: | Multi-band machine-learning framework for reliable 5–30 GHz LC-DCO synthesis in 22-nm FDSOI. |
|---|---|
| Authors: | Tsimpou, Panagiota1 (AUTHOR) ptsimpou@physics.auth.gr, Michailidis, Anastasios1,2 (AUTHOR), Noulis, Thomas1,2 (AUTHOR) |
| Source: | Integration: The VLSI Journal. Jul2026, Vol. 109, pN.PAG-N.PAG. 1p. |
| Subjects: | Machine learning, Frequency synthesizers, Phase noise measurement, Data augmentation, Semiconductor technology |
| Abstract: | Reliable millimeter-wave frequency synthesis requires oscillator designs that combine wide tuning coverage, low phase noise, and stable operation over broad frequency spans. This work introduces a machine-learning-driven framework for the automated design of LC Digitally Controlled Oscillators (DCOs) covering the 5–30 GHz range in 22-nm FDSOI technology. Surrogate models based on gradient-boosted ensembles are trained to predict oscillation frequency and phase noise directly from device dimensions, tank parameters, and bias conditions, enabling efficient navigation of the multi-dimensional design space. A targeted data-augmentation strategy enhances model generalization throughout the entire operating spectrum, while a frequency-aware decomposition further improves accuracy across the full range of synthesized oscillators. The proposed synthesis algorithm translates user-defined specifications, such as center frequency, tuning range, and phase-noise limits, into feasible transistor-level parameter sets and reconstructs the corresponding tuning boundaries via calibrated tank-capacitance adjustment. The automatically generated designs exhibit strong agreement with schematic and post-layout simulations, achieving wide tuning coverage and competitive phase noise with minimal deviation from predicted values. The results demonstrate that data-driven modeling supports reproducible, scalable, and specification-centric DCO design, offering a systematic alternative to conventional manual procedures and significantly reducing design effort in millimeter-wave oscillator development. • Fully automated, specification-driven LC-DCO synthesis (5–30 GHz) in 22-nm FDSOI. technology. • Multi-band Machine Learning surrogate models for frequency & phase-noise prediction. • Targeted data augmentation & frequency-aware modeling, achieving mm-wave accuracy. • Automated spec-to-transistor level DCO design, removing manual sweeps & tuning. • Strong agreement across predicted, schematic & post-layout results. [ABSTRACT FROM AUTHOR] |
| Copyright of Integration: The VLSI Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194605774 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multi-band machine-learning framework for reliable 5–30 GHz LC-DCO synthesis in 22-nm FDSOI. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tsimpou%2C+Panagiota%22">Tsimpou, Panagiota</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ptsimpou@physics.auth.gr</i><br /><searchLink fieldCode="AR" term="%22Michailidis%2C+Anastasios%22">Michailidis, Anastasios</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Noulis%2C+Thomas%22">Noulis, Thomas</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Integration%3A+The+VLSI+Journal%22">Integration: The VLSI Journal</searchLink>. Jul2026, Vol. 109, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Frequency+synthesizers%22">Frequency synthesizers</searchLink><br /><searchLink fieldCode="DE" term="%22Phase+noise+measurement%22">Phase noise measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Semiconductor+technology%22">Semiconductor technology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Reliable millimeter-wave frequency synthesis requires oscillator designs that combine wide tuning coverage, low phase noise, and stable operation over broad frequency spans. This work introduces a machine-learning-driven framework for the automated design of LC Digitally Controlled Oscillators (DCOs) covering the 5–30 GHz range in 22-nm FDSOI technology. Surrogate models based on gradient-boosted ensembles are trained to predict oscillation frequency and phase noise directly from device dimensions, tank parameters, and bias conditions, enabling efficient navigation of the multi-dimensional design space. A targeted data-augmentation strategy enhances model generalization throughout the entire operating spectrum, while a frequency-aware decomposition further improves accuracy across the full range of synthesized oscillators. The proposed synthesis algorithm translates user-defined specifications, such as center frequency, tuning range, and phase-noise limits, into feasible transistor-level parameter sets and reconstructs the corresponding tuning boundaries via calibrated tank-capacitance adjustment. The automatically generated designs exhibit strong agreement with schematic and post-layout simulations, achieving wide tuning coverage and competitive phase noise with minimal deviation from predicted values. The results demonstrate that data-driven modeling supports reproducible, scalable, and specification-centric DCO design, offering a systematic alternative to conventional manual procedures and significantly reducing design effort in millimeter-wave oscillator development. • Fully automated, specification-driven LC-DCO synthesis (5–30 GHz) in 22-nm FDSOI. technology. • Multi-band Machine Learning surrogate models for frequency & phase-noise prediction. • Targeted data augmentation & frequency-aware modeling, achieving mm-wave accuracy. • Automated spec-to-transistor level DCO design, removing manual sweeps & tuning. • Strong agreement across predicted, schematic & post-layout results. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Integration: The VLSI Journal is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.vlsi.2026.102754 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Frequency synthesizers Type: general – SubjectFull: Phase noise measurement Type: general – SubjectFull: Data augmentation Type: general – SubjectFull: Semiconductor technology Type: general Titles: – TitleFull: Multi-band machine-learning framework for reliable 5–30 GHz LC-DCO synthesis in 22-nm FDSOI. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tsimpou, Panagiota – PersonEntity: Name: NameFull: Michailidis, Anastasios – PersonEntity: Name: NameFull: Noulis, Thomas IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01679260 Numbering: – Type: volume Value: 109 Titles: – TitleFull: Integration: The VLSI Journal Type: main |
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