Balanced multi-specification indirect testing of analog circuits via heterogeneous dual-level ensemble regression

Indirect testing reduces analog integrated-circuit test cost by predicting electrical specifications from low-cost response-derived features. In practical multi-specification flows, target difficulty is often uneven: easy labels can dominate mean accuracy, whereas difficult labels determine test-flow reliability. This paper formulates multi-specification indirect testing as a balanced regression problem evaluated by mean accuracy, worst-target performance, and cross-target stability. We propose Heterogeneous Dual-Level Ensemble Regression (HDLER), a target-wise framework that trains complementary base regressors independently, fuses them by stacking with feature passthrough, and adaptively blends the stacked output with base-model averaging using out-of-fold validation. On a simulated operational-amplifier dataset with 5000 samples, 58 features, and 11 specifications, HDLER achieves a mean target-wise test R2 of 0.9508 across five random seeds, outperforming stacking alone (0.9339) and simple averaging (0.7843). Relative to stacking, it improves worst-target R2 from 0.6487 to 0.6939, increases difficult-target mean R2 from 0.8264 to 0.8659, and reduces the across-target standard deviation from 0.1120 to 0.0952. A measured-data validation on commercial OPA314 devices further obtains a mean R2 of 0.9874 and a worst-target R2 of 0.9670. The results show that target-aware fusion improves multi-specification reliability when performance is limited by the weakest labels.