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Machine-Learning Variation-Aware Co-Design for Analog Linearization of VCO-ADCs
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DOI：10.1109/OJCAS.2026.3676608.png)
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Abstract
En 中文
This article introduces a machine-learning (ML)-driven co-design and optimization framework for open-loop analog linearization of VCO-ADCs, especially operating in the low supply-voltage (VDD) regime, and demonstrated on a new coupled-oscillator-ensemble (COE) circuit architecture. The high-dimensional optimization space associated with the embedded linearization tuning 'knobs' of the VCOs renders exhaustive transistor-level search infeasible. To address this challenge, a deep neural network (DNN) surrogate is trained on a compact set of transistor-level transient simulations capturing the COE's composite voltage-to-frequency (V -to- f) characteristics. This surrogate enables rapid exploration of the vast tuning-knob landscape and steers an evolutionary genetic algorithm (GA) toward configurations that optimize harmonic distortion (HD). To further enhance HD-prediction robustness and incorporate VDD variation awareness, the framework integrates advanced ML techniques. Monte Carlo (MC)-perturbed GA optimization improves resilience to parameter uncertainty, while a stacked-ensemble surrogate network, constructed from expert-VDD-specific base models fused with a hybrid encoder-combiner meta-learner, facilitates accurate VDD-aware predictions. The resulting optimized tuning settings are transferred to a Cadence simulation environment for transistor-level verification of the VCO-ADC, achieving a mean third-order harmonic distortion (HD3) of 55 dB. Across a wide 0.4-0.6 V supply range (approximate to 40\%$ variation), HD3 remains above 50 dB through the DNN's supply-adaptive dynamic programming of the tuning-knob values.
Keywords:
Feeds
Circuits
Integrated circuits
Oscillators
Voltage-controlled oscillators
Circuits and systems
Transconductors
Ring oscillators
Integrated circuit synthesis
CMOS technology
Analog-to-digital converter (ADC)
analog linearization
calibration
co-design
coupled oscillator ensembles (COE)
genetic algorithms
gradient-descent
low supply voltage
machine-learning
meta-learner
neural networks
optimization
robustness
stacked ensemble
surrogate modelling
variation-aware
voltage-controlled oscillator (VCO)
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2.4
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4.5K
Citations:
387
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