Method Selection for Analog Circuit Calibration: Predictive Evaluation of GBDT and Classical Interpolation Methods

Polynomial fitting is widely used for analog circuit calibration but is sensitive to polynomial order and can become numerically unstable at high orders. This paper presents a systematic comparison between a lightweight Gradient Boosting Decision Tree (GBDT) approach and classical interpolation methods for analog circuit calibration, using two case studies: a 0–100 dB variable-gain amplifier (VGA) and a low-dropout regulator (LDO) under three operating conditions. Unlike prior work that reports only fitting accuracy, we evaluate predictive generalization using leave-one-out and repeated random hold-out validation, with all hyperparameters selected on the training set only. The experimental results show that method selection depends critically on data characteristics and that the advantage of GBDT is far less general than commonly assumed for the smooth one-dimensional calibration datasets investigated in this work. On smooth, one-dimensional calibration data—including the VGA and all three LDO sweeps—polynomial fitting, piecewise-linear interpolation, and cubic splines consistently achieve lower held-out error than GBDT. For example, on the VGA dataset, the held-out MAE of GBDT is 1.02 dB, whereas polynomial fitting achieves 0.0072 dB and piecewise-linear interpolation 0.0055 dB. GBDT does not provide a systematic advantage under sparse sampling in the one-dimensional cases studied here; its apparent zero-error behavior in earlier fitting-only evaluations is shown to reflect training-set memorization rather than predictive accuracy. The trained GBDT models remain lightweight (97–250 KB) with sub-millisecond inference time, but on the smooth, low-dimensional data considered in this work, classical interpolation methods are the preferred choice. This work provides practical, data-driven guidance for method selection in analog circuit calibration and clarifies the conditions under which tree-based models are and are not appropriate.