This paper builds on our Uncertainty-Guided Live Measurement Sequencing (UGLMS) method. UGLMS is a closed-loop test strategy that adaptively selects SAR ADC code edges based on model uncertainty and refines a behavioral mismatch model in real time via an Extended Kalman Filter (EKF), eliminating full-range sweeps and offline post-processing. We introduce an enhanced UGLMS that delivers significantly faster test runtimes while maintaining estimation accuracy. First, a rank-1 EKF update replaces costly matrix inversions with efficient vector operations, and a measurement-aligned covariance-inflation strategy accelerates convergence under unexpected innovations. Second, we extend the static mismatch model with a low-order carrier polynomial to capture systematic nonlinearities beyond pure capacitor mismatch. Third, a trace-based termination adapts test length to convergence, preventing premature stops and redundant iterations. Simulations show the enhanced UGLMS reconstructs full Integral- and Differential-Non-Linearity (INL/DNL) in just 36 ms for 16-bit and under 70 ms for 18-bit ADCs (120 ms with the polynomial extension). Combining the faster convergence from covariance inflation with reduced per-iteration runtime from the rank-1 EKF update, the method reaches equal accuracy 8x faster for 16-bit ADCs. These improvements enable real-time, production-ready SAR ADC linearity testing.

翻译：本文基于我们先前提出的不确定性引导实时测量序列（UGLMS）方法展开研究。UGLMS是一种闭环测试策略，它根据模型不确定性自适应地选择逐次逼近型模数转换器（SAR ADC）的码字边沿，并通过扩展卡尔曼滤波器（EKF）实时优化行为级失配模型，从而避免了全量程扫描和离线后处理。我们在此提出一种增强型UGLMS方法，在保持估计精度的同时，显著缩短了测试运行时间。首先，采用秩-1 EKF更新以高效的向量运算替代了昂贵的矩阵求逆，并结合一种测量对齐的协方差膨胀策略，以在出现意外新息时加速收敛。其次，我们在静态失配模型中引入了低阶载波多项式，以捕捉超出纯电容失配的系统性非线性。第三，基于迹的终止条件使测试长度能够自适应收敛状态，避免了过早停止和冗余迭代。仿真结果表明，增强型UGLMS仅需36毫秒即可重建16位ADC的完整积分非线性（INL）和微分非线性（DNL），对于18位ADC则不到70毫秒（若包含多项式扩展则为120毫秒）。结合协方差膨胀带来的更快收敛速度与秩-1 EKF更新降低的每次迭代运行时间，该方法在16位ADC上以8倍的速度达到了同等精度。这些改进为实现实时、可用于生产的SAR ADC线性度测试提供了可能。