Kolmogorov-Arnold Networks-Based Calibration for Single-Channel ADCs: High-Precision Nonlinear Code Synthesis With Low Power Consumption

Yutao Peng, Xizhu Peng, Hu Wang, Dongbing Fu, Yabo Ni, Can Zhu, Boyuan Zhang, Lei Chen, Zhe Hu, Zhifei Lu, He Tang, Mingqiang Guo

Abstract: This paper presents a novel calibration scheme for single-channel SAR, pipelined and pipelined-SAR ADCs using Kolmogorov-Arnold networks (KANs). In the proposed scheme, a multi-sample KAN (MS-KAN) is designed to realize nonlinear code synthesis (NLCS), achieving effective calibration for general nonlinear errors. The MS-KAN-based calibrator can be converted into an analytical expression, making the calibration process transparent, with stronger interpretability, predictability and reliability compared to previous neural network-based calibration algorithms, and assisting in the analysis of ADC nonidealities. Meanwhile, the proposed scheme achieves high calibration performance with low hardware overhead. The proposed scheme also requires much fewer training samples, thereby reducing the effort required for both chip testing and network training. The MS-KAN-based calibrator is verified with two silicon-proven ADCs, a 14-bit 1.3 GS/s pipelined ADC and a 10-bit 700MS/s SAR ADC. Measurement results show that SFDR is improved by 11.5 dB to 30.9 dB after calibration. The quantized calibrators are implemented on both FPGA and 28nm CMOS technology, where a piecewise polynomial (PWP) method is adopted to simplify the implementation of the calibrator. The post-layout simulation results show that the calibrator for the real-time calibration of the pipelined ADC consumes only 6.32 mW, while the calibrator for the SAR ADC consumes 2.42 mW.

External IDs:dblp:journals/tcasI/PengPWFNZZCHLTG25