针对高精度逐次逼近型模数转换器中的电容失配问题，本文设计一种基于正弦信号输入的前台校准技术，通过采集内核数据进行多次拟合使其SNR满足指标后，获取电容失配寄存器值并进行OTP烧录，在不影响模数转换器采样速率的同时能够有效提高转换精度和信噪比。该前台校准技术是在LMS算法基础上变化而来，通过SAR ADC采集16K内核数据经Matlab非线性最小二乘拟合，借鉴LMS算法思想对该残差信号进行多次迭代，每次迭代都会对ADC的每一位权重进行相应调整，约1000次迭代后SNR 88 dB，SFDR 98dB，分别比校准前提高22dB与17dB，仿真和测试结果表明，该校准技术能够有效提升ADC输出性能。

Addressing the capacitor mismatch issue in high-precision successive approximation analog-to-digital converters (ADCs), this paper designs a foreground calibration technique based on sine signal input. By collecting kernel data for multiple fitting to ensure the signal-to-noise ratio (SNR) meets the specifications, the capacitor mismatch register values are obtained and OTP programming is performed. This effectively improves conversion accuracy and SNR without affecting the ADC sampling rate. This foreground calibration technique is derived from the Least Mean Squares (LMS) algorithm. It collects 16K kernel data from the SAR ADC and performs nonlinear least squares fitting using Matlab. Drawing on the idea of the LMS algorithm, the residual signal undergoes multiple iterations, with each iteration adjusting the weight of each bit of the ADC accordingly. After approximately 1000 iterations, the SNR reaches 88 dB and the spurious-free dynamic range (SFDR) is 98 dB, which are 22 dB and 17 dB higher than before calibration, respectively. Simulation and test results show that this calibration technique effectively enhances the output performance of the ADC.