Machine Learning-Assisted Design of FinFET-Based Operational Amplifiers and LC Oscillators at the 22 nm Node: A Review
Keywords:
FinFET, High-k dielectric, Operational amplifier, LC oscillator, Machine learning, Linear regression, Analog design automationAbstract
The transition from planar CMOS to fin field-effect transistor (FinFET) technology has reshaped analog and radio-frequency circuit design, offering superior electrostatic control, reduced leakage, and higher intrinsic gain at the 22 nm node, but at the cost of quantised device widths, larger parasitic capacitances, and a design space that is expensive to explore through simulation alone. This paper reviews the convergence of two research directions: FinFET-based analog building blocks, particularly two-stage operational amplifiers realised with high-k gate dielectrics and inductor-capacitor (LC) oscillators, and the growing use of machine learning to model and optimise these circuits. The review first summarises FinFET device physics, high-k/metal-gate integration, and the analog figures of merit that distinguish FinFETs from planar devices. It then examines reported 22 nm FinFET operational amplifiers and LC oscillator designs, and surveys machine learning techniques ranging from linear regression frequency predictors to neural-network compact models, Bayesian optimisation, and reinforcement-learning-based transistor sizing. Comparative tables summarise the method, technology, and key contribution of representative works, and a design flow that couples simulation-derived datasets with learned surrogate models is presented. The synthesis shows that simple regression models already give accurate and interpretable predictions for well-conditioned problems such as LC tank frequency, whereas data-hungry neural and reinforcement-learning approaches remain limited by simulation cost, process variability, and transferability across technology nodes. Open challenges and future research directions are outlined.
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