Boosting

  • No Comments

A 12 b 180-MS/s Pipelined-SAR ADC With a Low-Power Calibration-Free RA and Hardware-Efficient SAR Logic

A 12 b 180-MS/s Pipelined-SAR ADC With a Low-Power Calibration-Free RA and Hardware-Efficient SAR Logic 150 150
Author(s): Guo Wei, Rui Zhang, Fule Li, Hanjun Jiang, Zhihua Wang

Abstract:

This letter presents a 12-bit, 180-MS/s pipelined-SAR ADC in 65-nm CMOS. To eliminate the complex interstage gain-error calibration for a fast-response characteristic, a high-gain residue amplifier (RA) featuring a two-stage gain-boosting architecture is proposed. By removing the tail current, the RA significantly alleviates slew-rate and voltage headroom limitations. The …

Published in: IEEE Solid-State Circuits Letters
Page(s): 186 – 189
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3697220
Publisher: IEEE

View on IEEE Xplore

  • No Comments

A Noise-Shaping SAR-Based ExG Sensing Frontend With Dynamic Input-Impedance Boosting and Prediction-Assisted Mismatch-Shaping DEM

A Noise-Shaping SAR-Based ExG Sensing Frontend With Dynamic Input-Impedance Boosting and Prediction-Assisted Mismatch-Shaping DEM 150 150
Author(s): Yiming Han, Linran Zhao, Yaoyao Jia

Abstract:

This article presents a noise-shaping successive approximation register (NS-SAR)-based direct-digitizing electrophysiological (ExG) sensing frontend (SFE) fabricated in a standard 180-nm CMOS process. To address the challenges of large motion artifacts and high electrode–tissue impedance (ETI), we propose three key innovations in our proposed SFE that enable accurate ExG …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 15
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3674986
Publisher: IEEE

View on IEEE Xplore

  • No Comments

Energy-Efficient Reconfigurable XGBoost Inference Accelerator With Modular Unit Trees via Selective Node Execution and Data Movement

Energy-Efficient Reconfigurable XGBoost Inference Accelerator With Modular Unit Trees via Selective Node Execution and Data Movement 150 150
Author(s): Chang Eun Song, Yidong Li, Amardeep Ramnani, Pulkit Agrawal, Purvi Agrawal, Sung-Joon Jang, Sang-Seol Lee, Tajana Rosing, Mingu Kang

Abstract:

The extreme gradient boosting (XGBoost) has emerged as a powerful AI algorithm, achieving high accuracy and winning multiple Kaggle competitions in various tasks including medical diagnosis, recommendation systems, and autonomous driving. It has great potential for running on edge devices due to its binary tree-based simple computing kernel, offering unique …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3607896
Publisher: IEEE

View on IEEE Xplore

Search this website