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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]
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Database: Engineering Source
Description
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]
ISSN:01679260
DOI:10.1016/j.vlsi.2026.102754