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A Behavior-Centric Methodology for High-Performance Amplifier Sizing
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DOI：10.1109/tcad.2026.3725982.png)
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Abstract
En 中文
Current automated analog sizing relies heavily on simulation-based “opaque-box” optimization in the size domain. This forces the optimizer to directly navigate a highly non-linear mapping, failing to bridge the semantic gap between physical device sizes and high-level circuit specifications. To mitigate this non-linearity, we propose a behavior-centric sizing framework that decouples the optimization process into an efficient search over behavioral parameters (BPs) and a subsequent BP-to-size direct mapping for a given process. Specifically for amplifiers, the BP set (gm, fT ,AV ) encapsulates drive strength, high-frequency speed, and intrinsic gain, utilizing parameters fundamental to small-signal analysis and effectively transforming the optimization into a more linear and well-behaved search space. Exploiting this well-behaved space, we leverage Kolmogorov-Arnold Networks (KANs) to build data-efficient, symbolic surrogate models that map BPs to circuit performance, and employ a lookup-table (LUT)-based back-mapping to cleanly instantiate the optimized BPs into device sizes. Implemented in the open-source tool ‘Amplifier Copilot’, the framework generated a 7,200-point design database across 22 topologies, four process nodes, and both low-voltage and high-voltage device options. Silicon validation in a 22 nm CMOS process confirms that the measured silicon performance aligns with simulation predictions, validating the practical feasibility of the proposed framework. The Amplifier Copilot is available at https://github.com/AmpCopilot/Amplifier-Copilot.
Keywords:
Analog design automation
amplifier sizing
Kolmogorov-Arnold networks
symbolic regression
knowledge-driven design
transistor behavioral representation
amplifier copilot
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