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Open Access

A three-stage pipeline readout circuit with on-chip self-calibration for computing-in-memory

Jiantao Chen, Yang Qiao, Zhiguo Yu( ), Xiaofeng Gu
Jiangsu Provincial key Laboratory of MEMS Sensors and ASIC Manufacturing Technology, School of Integrated Circuits, Jiangnan University, Wuxi 214401, China
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Abstract

This paper presents a high-precision three-stage pipeline readout circuit for computing-in-memory (CIM) architectures with on-chip self-calibration. The proposed readout circuit addresses the challenges of precision errors in analog computing and enhances computing throughput. A current subtraction error calibration is proposed to effectively compensate for current mirror mismatches, ensuring accurate bypass current by employing negative feedback and capacitor storage. In addition, a resistance variation calibration is proposed to suppress the non-linearity of current-to-voltage conversion circuits with ADC-assisted offset detection. Furthermore, a three-stage pipeline scheme is proposed to enhance computing throughput by parallel processing. Simulated in a 55 nm process, the proposed current subtraction error calibration improves the precision of adjacent column current subtraction by 58.3% compared to the case without calibration. Moreover, the computing throughput of the proposed readout circuit reaches 1908 giga operations per second (GOPS). Under 8-bit weight input and 8-bit output quantization, the proposed readout circuit demonstrates significant improvements in inference accuracy on MNIST, CIFAR-10, and ImageNet datasets by 7.6%, 6.6%, and 8.9%, respectively.

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Journal of Measurement Science and Instrumentation
Pages 446-458

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Cite this article:
Chen J, Qiao Y, Yu Z, et al. A three-stage pipeline readout circuit with on-chip self-calibration for computing-in-memory. Journal of Measurement Science and Instrumentation, 2026, 17(3): 446-458. https://doi.org/10.62756/jmsi.1674-8042.2026038

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Received: 31 December 2025
Revised: 03 February 2026
Accepted: 05 March 2026
Published: 01 September 2026
© The Author(s) 2026.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.