IEEE transactions on biomedical circuits and systems最新文献_第6页

IEEE Transactions on Biomedical Circuits and Systems Publication Information IEEE生物医学电路和系统汇刊信息

IF 4.9

IEEE transactions on biomedical circuits and systems

Pub Date : 2025-08-05 DOI: 10.1109/TBCAS.2025.3576469

引用次数: 0

Erratum to “A 43.5dB Gain Unipolar a-IGZO TFT Amplifier with Parallel Bootstrap Capacitor for Bio-signals Sensing Applications” “用于生物信号传感应用的带并联自引导电容的43.5dB增益单极A - igzo TFT放大器”的校误

IF 4.9

IEEE transactions on biomedical circuits and systems

Pub Date : 2025-08-05 DOI: 10.1109/TBCAS.2025.3583095

Mingjian Zhao;Laiqing Li;Rui Liu;Bin Li;Rongsheng Chen;Zhaohui Wu

In [1], a critical labeling error was identified in Fig. 21, where the x-axis was incorrectly labeled “−50 ms to 50 ms” instead of the correct range “0 s to 5 s” (reflecting the actual ECG data duration). This discrepancy resulted from unintentional reuse of a plotting template and insufficient validation during proofing. While the underlying ECG waveform data remains accurate, the mislabeled scale misrepresents the signal’s temporal characteristics. The Fig. 21 should be corrected as follows:

在[1]中，在图21中发现了一个关键的标记错误，其中x轴被错误地标记为“−50 ms至50 ms”，而不是正确的范围“0 s至5 s”（反映实际ECG数据持续时间）。这种差异是由于绘图模板的无意重用和打样过程中的验证不足造成的。虽然潜在的心电图波形数据仍然准确，但错误标记的尺度错误地表示了信号的时间特征。图21应修改如下：

引用次数: 0

A Fast Electrochemical Impedance Spectroscopy With a Square Wave as Excitation Signal for Impedance-Based Biomedical Applications. 基于阻抗的生物医学应用中以方波为激励信号的快速电化学阻抗谱。

IF 4.9

IEEE transactions on biomedical circuits and systems

Pub Date : 2025-08-01 DOI: 10.1109/TBCAS.2025.3579698

Zhongzheng Wang, Han Shao, Alan O'Riordan, Javier Higes-Marquez, Ivan O'Connell, Daniel O'Hare

This paper introduces a fast, high-accuracy methodology for conducting Electrochemical Impedance Spectroscopy (EIS) based on Fast Fourier Transform (FFT), to meet the requirements of portable, real-time biomedical impedance-based detections with Ultra-Microband (UMB) sensor. Instead of using white noise-like wideband signals as in conventional FFT-based EIS, the proposed method uses a square wave as the excitation signal, which achieves a fast, accurate EIS measurement, but no longer requires complex circuits like high-resolution DACs or frequency mixers for the signal generation. This work starts with the theoretical justification for treating the sensor as a Linear Time-Invariant (LTI), then the practical linear region for operating the sensor as an LTI system is experimentally verified and determined, which enables the capacity of employing the harmonics of a square wave for EIS measurements. A dynamic model of the charge-transfer resistance together with an approximated of the Constant Phase Element (CPE) are implemented with Verilog-A for simulations, and a circuit consisting of a control amplifier and a Trans-Impedance Amplifier (TIA) is designed and fabricated with 65 nm CMOS for validating its on-chip feasibility. This work shortens the EIS measurement time by 91.7% in a frequency sweep range from 0.5 Hz to 500 Hz, with only 2.73% average Mean Absolute Percentage Error (MAPE), compared to a commercial electrochemical instrument AutoLab, with five pre-modified electrodes across four different concentrations of Ferrocene Carboxylic Acid (FcCOOH), demonstrating this method is suitable for portable, real-time label-free EIS biomedical detections and applications.

本文介绍了一种基于快速傅里叶变换（FFT）的快速、高精度电化学阻抗谱（EIS）方法，以满足便携式、实时生物医学阻抗检测的要求。该方法不像传统的基于fft的EIS那样使用类白噪声的宽带信号，而是使用方波作为激励信号，实现了快速、准确的EIS测量，但不再需要高分辨率dac或混频器等复杂电路来产生信号。这项工作从将传感器视为线性时不变（LTI）的理论论证开始，然后通过实验验证和确定将传感器作为LTI系统操作的实际线性区域，这使得能够使用方波的谐波进行EIS测量。利用Verilog-A软件建立了电荷转移电阻的动态模型和恒相元件（CPE）的近似模型进行仿真，并采用65 nm CMOS设计和制作了由控制放大器和跨阻抗放大器组成的电路，验证了其片上可行性。与AutoLab的商用电化学仪器相比，这项工作在0.5 Hz至500 Hz的频率扫描范围内将EIS测量时间缩短了91.7%，平均绝对百分比误差（MAPE）仅为2.73%，在四种不同浓度的二茂铁羧酸（FcCOOH）中使用了五个预修饰电极，证明了该方法适用于便携式，实时无标签的EIS生物医学检测和应用。

{"title":"A Fast Electrochemical Impedance Spectroscopy With a Square Wave as Excitation Signal for Impedance-Based Biomedical Applications.","authors":"Zhongzheng Wang, Han Shao, Alan O'Riordan, Javier Higes-Marquez, Ivan O'Connell, Daniel O'Hare","doi":"10.1109/TBCAS.2025.3579698","DOIUrl":"10.1109/TBCAS.2025.3579698","url":null,"abstract":"This paper introduces a fast, high-accuracy methodology for conducting Electrochemical Impedance Spectroscopy (EIS) based on Fast Fourier Transform (FFT), to meet the requirements of portable, real-time biomedical impedance-based detections with Ultra-Microband (UMB) sensor. Instead of using white noise-like wideband signals as in conventional FFT-based EIS, the proposed method uses a square wave as the excitation signal, which achieves a fast, accurate EIS measurement, but no longer requires complex circuits like high-resolution DACs or frequency mixers for the signal generation. This work starts with the theoretical justification for treating the sensor as a Linear Time-Invariant (LTI), then the practical linear region for operating the sensor as an LTI system is experimentally verified and determined, which enables the capacity of employing the harmonics of a square wave for EIS measurements. A dynamic model of the charge-transfer resistance together with an approximated of the Constant Phase Element (CPE) are implemented with Verilog-A for simulations, and a circuit consisting of a control amplifier and a Trans-Impedance Amplifier (TIA) is designed and fabricated with 65 nm CMOS for validating its on-chip feasibility. This work shortens the EIS measurement time by 91.7% in a frequency sweep range from 0.5 Hz to 500 Hz, with only 2.73% average Mean Absolute Percentage Error (MAPE), compared to a commercial electrochemical instrument AutoLab, with five pre-modified electrodes across four different concentrations of Ferrocene Carboxylic Acid (FcCOOH), demonstrating this method is suitable for portable, real-time label-free EIS biomedical detections and applications.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":"726-742"},"PeriodicalIF":4.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144328223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FPGA-Based Medical Image Processing Using Hardware-Software Co-Design Approach 基于fpga的医学图像处理软硬件协同设计方法。

IF 4.9

IEEE transactions on biomedical circuits and systems

Pub Date : 2025-08-01 DOI: 10.1109/TBCAS.2025.3594840

Abhishek Yadav;Vyom Kumar Gupta;Binod Kumar

This paper presents a field-programmable gate array (FPGA) based medical image processing framework using a hardware-software co-design approach for biomedical tasks such as Malaria and Pneumonia detection. The design is implemented on the AMD-Xilinx UltraScale+ MPSoC (ZCU104) FPGA, focusing on optimizing data movement between the Processing System (PS) and Programmable Logic (PL) through a customized high-level synthesis (HLS) process. Depth-wise convolution is employed to reduce computational complexity, while layer fusion is applied to optimize layer-wise execution, and custom cache is integrated to improve memory access efficiency. The accelerated architecture is integrated with AXI interconnects and tested using the PYNQ overlay process. The experimental results demonstrate that the proposed accelerator achieves a throughput of 298.22 FPS and 205.87 FPS for the detection of malaria and pneumonia, respectively. The proposed design significantly improves energy efficiency, consuming 14.62 mJ/img for the detection of malaria and 23.89 mJ/img for the detection of pneumonia. Compared to alternative hardware platforms like Raspberry Pi with Coral TPU, the FPGA-based implementation offers superior performance, achieving 8.3

$boldsymbol{times}$

higher throughput and 4.3

$boldsymbol{times}$

better energy efficiency, making it well-suited for real-time medical image processing applications.

本文提出了一种基于现场可编程门阵列（FPGA）的医学图像处理框架，该框架采用软硬件协同设计方法用于疟疾和肺炎检测等生物医学任务。该设计是在AMD-Xilinx UltraScale+ MPSoC (ZCU104) FPGA上实现的，重点是通过定制的高级合成（HLS）工艺优化处理系统（PS）和可编程逻辑（PL）之间的数据移动。采用深度卷积降低计算复杂度，采用层融合优化分层执行，集成自定义缓存提高内存访问效率。加速架构与AXI互连集成，并使用PYNQ覆盖过程进行测试。实验结果表明，该加速器检测疟疾和肺炎的吞吐量分别为298.22 FPS和205.87 FPS。该设计显著提高了能效，疟疾检测能耗为14.62 mJ/img，肺炎检测能耗为23.89 mJ/img。与其他硬件平台（如带有Coral TPU的树莓派）相比，基于fpga的实现提供了卓越的性能，实现了8.3倍的吞吐量和4.3倍的能效，使其非常适合实时医疗图像处理应用。

{"title":"FPGA-Based Medical Image Processing Using Hardware-Software Co-Design Approach","authors":"Abhishek Yadav;Vyom Kumar Gupta;Binod Kumar","doi":"10.1109/TBCAS.2025.3594840","DOIUrl":"10.1109/TBCAS.2025.3594840","url":null,"abstract":"This paper presents a field-programmable gate array (FPGA) based medical image processing framework using a hardware-software co-design approach for biomedical tasks such as Malaria and Pneumonia detection. The design is implemented on the AMD-Xilinx UltraScale+ MPSoC (ZCU104) FPGA, focusing on optimizing data movement between the Processing System (PS) and Programmable Logic (PL) through a customized high-level synthesis (HLS) process. Depth-wise convolution is employed to reduce computational complexity, while layer fusion is applied to optimize layer-wise execution, and custom cache is integrated to improve memory access efficiency. The accelerated architecture is integrated with AXI interconnects and tested using the PYNQ overlay process. The experimental results demonstrate that the proposed accelerator achieves a throughput of 298.22 FPS and 205.87 FPS for the detection of malaria and pneumonia, respectively. The proposed design significantly improves energy efficiency, consuming 14.62 mJ/img for the detection of malaria and 23.89 mJ/img for the detection of pneumonia. Compared to alternative hardware platforms like Raspberry Pi with Coral TPU, the FPGA-based implementation offers superior performance, achieving 8.3<inline-formula><tex-math>$boldsymbol{times}$</tex-math></inline-formula> higher throughput and 4.3<inline-formula><tex-math>$boldsymbol{times}$</tex-math></inline-formula> better energy efficiency, making it well-suited for real-time medical image processing applications.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"20 1","pages":"57-68"},"PeriodicalIF":4.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144765894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MulPi: A Multi-class and Patient-Independent Epileptic Seizure Classifier With Co-Designed Input-stationary Computing-in-SRAM. MulPi：一种多类别、独立于患者的癫痫发作分类器，在sram中协同设计输入静止计算。

IF 4.9

IEEE transactions on biomedical circuits and systems

Pub Date : 2025-08-01 DOI: 10.1109/TBCAS.2025.3579273

Bokyung Kim, Qijia Huang, Brady Taylor, Qilin Zheng, Jonathan Ku, Yiran Chen, Hai Li

Unprovoked seizures have threatened epilepsy patients over 70 million. Automated classification to detect and predict seizures could bring seizure-free lives to epilepsy patients, delivering them from fatal danger and increasing the quality of life. Authentic detection and prediction of seizures require 1) multi-class (Mul) and 2) patient-independent (Pi) classification. Recent implementable chips for seizure classification rarely satisfy the two requirements due to restricted resources in small chips; therefore, high efficiency is imperative along with accuracy. This paper introduces an efficient MulPi chip, fabricated for the first time to simultaneously fulfill multi-class and patient independence, based on a co-design approach. We develop a 5-layer convolutional neural network (CNN), MulPiCNN, with advanced training techniques for lightness and accuracy. At the hardware level, our SRAM-based chip leverages computing-in-memory (CIM) for efficiency. The fabricated MulPi chip is distinguished from prior CIMs in two folds, namely ISRW-CIM: a) input-stationary (IS) CIM for resource-saving, and b) row-wise (RW) computing to address a challenge of SRAM CIM, empowered by our novel 2T-Hadamard product unit (HPU). MulPi outperforms state-of-the-art chips with 98.5% sensitivity and 99.2% specificity, classifying in 0.12s and 0.348mm${}^{2}$.

无端发作威胁着7000多万癫痫患者。检测和预测癫痫发作的自动分类可以为癫痫患者带来无癫痫发作的生活，使他们摆脱致命的危险，提高生活质量。癫痫发作的真实检测和预测需要1)多类别（multi-class, Mul）和2)患者独立（patient-independent, Pi）分类。由于小型芯片资源有限，目前可实现的缉获物分类芯片很少能满足这两个要求；因此，高效率和准确性是必不可少的。本文介绍了一种基于协同设计方法的高效MulPi芯片，该芯片首次同时实现了多类和患者独立性。我们开发了一个5层卷积神经网络（CNN）， MulPiCNN，具有先进的轻量化和准确性训练技术。在硬件层面，我们基于sram的芯片利用内存计算（CIM）来提高效率。制造的MulPi芯片与之前的CIM有两方面的区别，即ISRW-CIM: a)用于节省资源的输入静止（is） CIM，以及b)行（RW）计算，以解决SRAM CIM的挑战，由我们新颖的2T-Hadamard产品单元（HPU）提供支持。MulPi以98.5%的灵敏度和99.2%的特异性优于最先进的芯片，分类时间为0.12秒和0.48 mm2。

{"title":"MulPi: A Multi-class and Patient-Independent Epileptic Seizure Classifier With Co-Designed Input-stationary Computing-in-SRAM.","authors":"Bokyung Kim, Qijia Huang, Brady Taylor, Qilin Zheng, Jonathan Ku, Yiran Chen, Hai Li","doi":"10.1109/TBCAS.2025.3579273","DOIUrl":"10.1109/TBCAS.2025.3579273","url":null,"abstract":"Unprovoked seizures have threatened epilepsy patients over 70 million. Automated classification to detect and predict seizures could bring seizure-free lives to epilepsy patients, delivering them from fatal danger and increasing the quality of life. Authentic detection and prediction of seizures require 1) multi-class (Mul) and 2) patient-independent (Pi) classification. Recent implementable chips for seizure classification rarely satisfy the two requirements due to restricted resources in small chips; therefore, high efficiency is imperative along with accuracy. This paper introduces an efficient MulPi chip, fabricated for the first time to simultaneously fulfill multi-class and patient independence, based on a co-design approach. We develop a 5-layer convolutional neural network (CNN), MulPiCNN, with advanced training techniques for lightness and accuracy. At the hardware level, our SRAM-based chip leverages computing-in-memory (CIM) for efficiency. The fabricated MulPi chip is distinguished from prior CIMs in two folds, namely ISRW-CIM: a) input-stationary (IS) CIM for resource-saving, and b) row-wise (RW) computing to address a challenge of SRAM CIM, empowered by our novel 2T-Hadamard product unit (HPU). MulPi outperforms state-of-the-art chips with 98.5% sensitivity and 99.2% specificity, classifying in 0.12s and 0.348mm${}^{2}$.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"PP ","pages":"756-766"},"PeriodicalIF":4.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Stimulus Artifact Suppression System With Fast Template Subtraction 一种新的快速模板减法刺激伪影抑制系统。

IF 4.9

IEEE transactions on biomedical circuits and systems

Pub Date : 2025-07-22 DOI: 10.1109/TBCAS.2025.3591110

Yirui Liu;Quanbei Chang;Xuhui Li;Xiao Liu

The presence of large stimulus artifact (SA) makes it difficult to perform concurrent stimulation and recording in retinal prostheses. This paper presents a novel template-based system for suppressing SA visible at the stimulation/recording electrodes. The template of SA has been derived by working out the full Randles impedance model whose expression in the frequency domain serves as the transfer function from the stimulus current to SA. A prototype ASIC has been fabricated in a 180-nm CMOS process and validated in saline. The template calculation framework utilizes a pipeline digital processing which achieves rapid template generation within 26.35 ms (25.6 ms for acquiring the SA waveform and 0.75 ms for computation) after the detection of the first stimulation phase. The real-time SA suppression is 20.2 dB and can be boosted to 44.3 dB with offline signal processing. The ASIC’s core occupies 0.43 mm². It consumes 8.27

$mu$

W and 30.83

$mu$

W in the normal amplification mode and SA suppression mode, respectively.

大刺激伪影（SA）的存在给视网膜假体的同步刺激和记录带来困难。本文提出了一种新的基于模板的抑制刺激/记录电极可见SA的系统。通过求解全Randles阻抗模型，得到了SA的模板，该模型在频域的表达式作为刺激电流到SA的传递函数。采用180nm CMOS工艺制作了ASIC原型，并在盐水中进行了验证。模板计算框架采用流水线数字处理，在检测到第一个刺激周期后，在26.35 ms（获取SA波形25.6 ms，计算0.75 ms）内实现快速模板生成。实时SA抑制为20.2 dB，通过离线信号处理可提高到44.3 dB。ASIC的核心面积为0.43 mm2。在正常放大模式和SA抑制模式下，功耗分别为8.27 μW和30.83 μW。

{"title":"A Novel Stimulus Artifact Suppression System With Fast Template Subtraction","authors":"Yirui Liu;Quanbei Chang;Xuhui Li;Xiao Liu","doi":"10.1109/TBCAS.2025.3591110","DOIUrl":"10.1109/TBCAS.2025.3591110","url":null,"abstract":"The presence of large stimulus artifact (SA) makes it difficult to perform concurrent stimulation and recording in retinal prostheses. This paper presents a novel template-based system for suppressing SA visible at the stimulation/recording electrodes. The template of SA has been derived by working out the full <italic>Randles impedance model whose expression in the frequency domain serves as the transfer function from the stimulus current to SA. A prototype ASIC has been fabricated in a 180-nm CMOS process and validated in saline. The template calculation framework utilizes a pipeline digital processing which achieves rapid template generation within 26.35 ms (25.6 ms for acquiring the SA waveform and 0.75 ms for computation) after the detection of the first stimulation phase. The real-time SA suppression is 20.2 dB and can be boosted to 44.3 dB with offline signal processing. The ASIC’s core occupies 0.43 mm2. It consumes 8.27 <inline-formula><tex-math>$mu$</tex-math></inline-formula>W and 30.83 <inline-formula><tex-math>$mu$</tex-math></inline-formula>W in the normal amplification mode and SA suppression mode, respectively.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 5","pages":"936-949"},"PeriodicalIF":4.9,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances and Challenges in Integrated Circuits for Electrochemical Sensing: Enabling Next-Generation Biomedical and Molecular Applications 电化学传感集成电路的进展与挑战：实现下一代生物医学和分子应用。

IF 4.9

IEEE transactions on biomedical circuits and systems

Pub Date : 2025-07-14 DOI: 10.1109/TBCAS.2025.3589027

Qiuyang Lin;Sander Crols;Aurojyoti Das;Marcel Zevenbergen;Wim Sijbers;Nick Van Helleputte;Carolina Mora Lopez

This manuscript provides a comprehensive review of the design, implementation, and advancements in integrated circuits (ICs) for electrochemical sensing, with a focus on biomedical and molecular applications. It begins by discussing the fundamental principles of electrochemical sensing and core modalities, including potentiometry, amperometry, impedimetry, and ISFET-based sensing, highlighting their unique requirements and challenges. A detailed analysis of state-of-the-art readout circuit architectures is presented, emphasizing strategies for achieving high dynamic range (DR), low noise, and enhanced stability while minimizing leakage currents. Both resistive and capacitive transimpedance amplifiers (TIAs) and current conveyor (CC)-based circuits are examined, exploring critical trade-offs between speed, power consumption, and noise performance. This review also discusses emerging applications such as DNA sequencing and molecular sensing, covering both ISFET and nanopore-based approaches, to showcase recent advancements in high-throughput, high-speed, and low-power interface circuit designs. By highlighting the challenges of the readout-circuit miniaturization, integration, and scalability, as well as the current limitations in existing approaches, this review provides a comprehensive synthesis of advancements in high-performance electrochemical readout architectures and their potential to address the evolving demands of modern biomedical applications.

该手稿提供了一个全面的审查，设计，实施，并在集成电路（ic）的电化学传感进步，重点是生物医学和分子应用。首先讨论了电化学传感的基本原理和核心模式，包括电位法、安培法、阻抗法和基于isfet的传感，突出了它们独特的要求和挑战。详细分析了最先进的读出电路架构，强调实现高动态范围（DR），低噪声和增强稳定性的策略，同时最大限度地减少泄漏电流。电阻和电容跨阻放大器（TIAs）和电流传送带（CC）为基础的电路进行了检查，探索速度，功耗和噪声性能之间的关键权衡。本文还讨论了诸如DNA测序和分子传感等新兴应用，涵盖了ISFET和基于纳米孔的方法，以展示高通量、高速和低功耗接口电路设计的最新进展。通过强调读出电路小型化、集成化和可扩展性的挑战，以及现有方法的局限性，本综述全面综合了高性能电化学读出架构的进展及其解决现代生物医学应用不断发展的需求的潜力。

{"title":"Advances and Challenges in Integrated Circuits for Electrochemical Sensing: Enabling Next-Generation Biomedical and Molecular Applications","authors":"Qiuyang Lin;Sander Crols;Aurojyoti Das;Marcel Zevenbergen;Wim Sijbers;Nick Van Helleputte;Carolina Mora Lopez","doi":"10.1109/TBCAS.2025.3589027","DOIUrl":"10.1109/TBCAS.2025.3589027","url":null,"abstract":"This manuscript provides a comprehensive review of the design, implementation, and advancements in integrated circuits (ICs) for electrochemical sensing, with a focus on biomedical and molecular applications. It begins by discussing the fundamental principles of electrochemical sensing and core modalities, including potentiometry, amperometry, impedimetry, and ISFET-based sensing, highlighting their unique requirements and challenges. A detailed analysis of state-of-the-art readout circuit architectures is presented, emphasizing strategies for achieving high dynamic range (DR), low noise, and enhanced stability while minimizing leakage currents. Both resistive and capacitive transimpedance amplifiers (TIAs) and current conveyor (CC)-based circuits are examined, exploring critical trade-offs between speed, power consumption, and noise performance. This review also discusses emerging applications such as DNA sequencing and molecular sensing, covering both ISFET and nanopore-based approaches, to showcase recent advancements in high-throughput, high-speed, and low-power interface circuit designs. By highlighting the challenges of the readout-circuit miniaturization, integration, and scalability, as well as the current limitations in existing approaches, this review provides a comprehensive synthesis of advancements in high-performance electrochemical readout architectures and their potential to address the evolving demands of modern biomedical applications.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 5","pages":"876-896"},"PeriodicalIF":4.9,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144639078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Fully-Integrated 0.068-mm3 Implantable Pressure Sensing Device With Wireless Energy Harvesting and Data Telemetry 具有无线能量收集和数据遥测功能的完全集成的0.068毫米3植入式压力传感装置。

IF 4.9

IEEE transactions on biomedical circuits and systems

Pub Date : 2025-07-04 DOI: 10.1109/TBCAS.2025.3586009

Zehua Lan;Jiahua Shi;Jiayue Hao;Zhihua Wang;Yanshu Guo;Hanjun Jiang

This paper reports a fully-integrated sub-0.1 mm³ wireless pressure sensing device for implantable applications. The miniature device integrates a customized system-on-a-chip (SoC) and an off-the-shelf half-bridge piezoresistive pressure transducer, eliminating off-chip passive components. The SoC mainly comprises a resistance-to-time converter, a 915 MHz inductively coupled energy harvester with an on-chip coil, and a backscatter telemetry. Key innovations enabling low power, small size and high precision include: (1) A source-input common-gate amplifier based R-V converter, that reuses the transducer’s bias current, (2) Advanced noise management via chopper stabilization and supply noise cancellation, and (3) A compact high-Q on-chip multi-layer stacked coil design for wireless link. The active circuits consume 9.75

$boldsymbol{mu}$

W, fully supplied by the energy harvested wirelessly through the on-chip coil. The sensing data is transmitted wirelessly to an external recorder through the RF backscatter link. Fabricated in a 65-nm CMOS technology, the SoC occupies a die area of 400 µm × 490 µm, and the entire fully-integrated sensor has a volume of only 0.068 mm³, enabling syringe injection through a ≤0.5 mm needle. Experiments with the sensing device covered by pork have demonstrated that the device can operate at an implant depth of up to 10 mm with excellent misalignment tolerance. It offers a pressure sensing resolution of 3.1 mmHg over a relative pressure range of 0-200 mmHg and a temperature sensing resolution of 0.18°C.

本文报道了一种用于植入式应用的完全集成的小于0.1 mm3的无线压力传感装置。该微型器件集成了定制的片上系统（SoC）和现成的半桥压阻压力传感器，消除了片外无源元件。SoC主要包括电阻-时间转换器、带片上线圈的915 MHz电感耦合能量采集器和背向散射遥测。实现低功耗、小尺寸和高精度的关键创新包括：(1)基于源输入共门放大器的R-V转换器，可重用换能器的偏置电流，(2)通过斩波稳定和电源噪声消除来进行先进的噪声管理，以及(3)用于无线链路的紧凑高q片上多层堆叠线圈设计。有源电路的功耗为9.75 μW，完全由通过片上线圈无线采集的能量提供。传感数据通过射频反向散射链路无线传输到外部记录器。该芯片采用65纳米CMOS技术制造，芯片面积为400 μm × 490 μm，整个全集成传感器的体积仅为0.068 mm3，可通过≤0.5 mm的针头进行注射器注射。用猪肉覆盖的传感装置进行的实验表明，该装置可以在植入深度达10毫米的情况下工作，具有出色的偏差容忍度。它提供3.1 mmHg的压力传感分辨率，相对压力范围为0-200 mmHg，温度传感分辨率为0.18°C。

{"title":"A Fully-Integrated 0.068-mm3 Implantable Pressure Sensing Device With Wireless Energy Harvesting and Data Telemetry","authors":"Zehua Lan;Jiahua Shi;Jiayue Hao;Zhihua Wang;Yanshu Guo;Hanjun Jiang","doi":"10.1109/TBCAS.2025.3586009","DOIUrl":"10.1109/TBCAS.2025.3586009","url":null,"abstract":"This paper reports a fully-integrated sub-0.1 mm3 wireless pressure sensing device for implantable applications. The miniature device integrates a customized system-on-a-chip (SoC) and an off-the-shelf half-bridge piezoresistive pressure transducer, eliminating off-chip passive components. The SoC mainly comprises a resistance-to-time converter, a 915 MHz inductively coupled energy harvester with an on-chip coil, and a backscatter telemetry. Key innovations enabling low power, small size and high precision include: (1) A source-input common-gate amplifier based R-V converter, that reuses the transducer’s bias current, (2) Advanced noise management via chopper stabilization and supply noise cancellation, and (3) A compact high-Q on-chip multi-layer stacked coil design for wireless link. The active circuits consume 9.75 <inline-formula><tex-math>$boldsymbol{mu}$</tex-math></inline-formula>W, fully supplied by the energy harvested wirelessly through the on-chip coil. The sensing data is transmitted wirelessly to an external recorder through the RF backscatter link. Fabricated in a 65-nm CMOS technology, the SoC occupies a die area of 400 µm × 490 µm, and the entire fully-integrated sensor has a volume of only 0.068 mm3, enabling syringe injection through a ≤0.5 mm needle. Experiments with the sensing device covered by pork have demonstrated that the device can operate at an implant depth of up to 10 mm with excellent misalignment tolerance. It offers a pressure sensing resolution of 3.1 mmHg over a relative pressure range of 0-200 mmHg and a temperature sensing resolution of 0.18°C.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"20 1","pages":"28-40"},"PeriodicalIF":4.9,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144565570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Differential Impedance Flow Cytometry Front-End With Baseline Current Cancellation 差分阻抗流式细胞仪前端与基线电流消除。

IF 4.9

IEEE transactions on biomedical circuits and systems

Pub Date : 2025-07-01 DOI: 10.1109/TBCAS.2025.3585089

Siyuan Yu;Louis Marun;Matthew L. Johnston

In this work, we present a high-performance analog front-end (AFE) circuit for impedance-based flow cytometry readout. The AFE is designed to interface to a three-electrode sensor topology using center electrode excitation and differential current output. To satisfy the needs of a differential high gain signal path, we propose a digitally tunable and calibrated cancellation current generation path to remove the baseline current injected into the transimpedance amplifier (TIA) stages. This prevents TIA saturation and allows for higher gain. Consequently, the AFE is more power efficient while maintaining better noise and interference rejection. The proposed circuit is designed and fabricated in a 180 nm CMOS process. It covers an excitation frequency range of 0.5 MHz to 10 MHz and consumes 15.6 mW during nominal operation. Digital calibration is implemented using an off-chip ADC and automated calibration algorithm. Measurement results show that at 1 MHz excitation, the AFE achieves 1.7 pA/

$sqrt{text{Hz}}$

input-referred current noise density with floating inputs. The AFE achieves detection of 3 um diameter particles in a microfluidic flow cell, demonstrating its performance and practicality for impedance flow cytometry.

在这项工作中，我们提出了一种高性能模拟前端（AFE）电路，用于基于阻抗的流式细胞术读出。该AFE被设计为接口到三电极传感器拓扑使用中心电极激励和差动电流输出。为了满足差分高增益信号路径的需求，我们提出了一种数字可调谐和校准的抵消电流产生路径，以消除注入到跨阻放大器（TIA）级的基线电流。这可以防止TIA饱和，并允许更高的增益。因此，AFE更节能，同时保持更好的噪声和干扰抑制。该电路采用180nm CMOS工艺设计和制作。它的激励频率范围为0.5MHz至10MHz，在标称运行时消耗15.6mW。采用片外ADC和自动校准算法实现数字校准。测量结果表明，在1MHz激励下，具有浮动输入的AFE可达到$1.7 text{pA}/sqrt{text{Hz}}$输入参考电流噪声密度。该AFE在微流控流式细胞中实现了直径3um的颗粒检测，证明了其在阻抗流式细胞术中的性能和实用性。

{"title":"A Differential Impedance Flow Cytometry Front-End With Baseline Current Cancellation","authors":"Siyuan Yu;Louis Marun;Matthew L. Johnston","doi":"10.1109/TBCAS.2025.3585089","DOIUrl":"10.1109/TBCAS.2025.3585089","url":null,"abstract":"In this work, we present a high-performance analog front-end (AFE) circuit for impedance-based flow cytometry readout. The AFE is designed to interface to a three-electrode sensor topology using center electrode excitation and differential current output. To satisfy the needs of a differential high gain signal path, we propose a digitally tunable and calibrated cancellation current generation path to remove the baseline current injected into the transimpedance amplifier (TIA) stages. This prevents TIA saturation and allows for higher gain. Consequently, the AFE is more power efficient while maintaining better noise and interference rejection. The proposed circuit is designed and fabricated in a 180 nm CMOS process. It covers an excitation frequency range of 0.5 MHz to 10 MHz and consumes 15.6 mW during nominal operation. Digital calibration is implemented using an off-chip ADC and automated calibration algorithm. Measurement results show that at 1 MHz excitation, the AFE achieves 1.7 pA/<inline-formula><tex-math>$sqrt{text{Hz}}$</tex-math></inline-formula> input-referred current noise density with floating inputs. The AFE achieves detection of 3 um diameter particles in a microfluidic flow cell, demonstrating its performance and practicality for impedance flow cytometry.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"20 1","pages":"15-27"},"PeriodicalIF":4.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CMOS LIF Neurons With Local Membrane Dynamic Biasing Based on Reciprocal Inhibition for Self-Oscillatory Neural Networks 基于互反抑制的自振荡神经网络局部膜动态偏置CMOS LIF神经元。

IF 4.9

IEEE transactions on biomedical circuits and systems

Pub Date : 2025-06-25 DOI: 10.1109/TBCAS.2025.3583093

Mannhee Cho;Minil Kang;Minseong Um;Hangue Park;Hyung-Min Lee

This paper presents a CMOS-based neuron network that can emulate self-oscillatory biasing behaviors found in biological neural oscillator models. Based on leaky integrate-and-fire (LIF) neuron models, the proposed neuron circuit adopts the concept of reciprocal inhibitory network and synaptic fatigue as well as excitatory drive stimulation for replicating extracellular fluidic biasing of membrane potentials. On top of the base neuron circuit, an excitation integrator integrates positive and negative excitatory input spikes to stimulate the membrane potential bias, and a bias controller receives inhibitory drive input and generates output inhibitory drives depending on the membrane potential bias level. The proposed networks of multiple neurons with inhibitory connections can generate oscillating membrane potential biases, which can be used as local dynamic thresholds for neuron spike firing, resulting in self-patterned output spikes such as switching or dynamic firing rate patterns. The proposed neuron network was implemented with 250-nm CMOS process operating at the supply voltage of 2.5 V and consuming average power of 99.31

$boldsymbol{mu}$

W per neuron during full operation. Operation waveforms were measured in various input conditions which can produce multiple output patterns. Variances in output signals due to process variation were measured from 32 neurons to verify the stability of operation, showing the standard deviation of 18% in the membrane potential gain per input spike and 12% in oscillation periods of the membrane potential bias. The results verified that the proposed neuron network can replicate the self-oscillatory behaviors of biological neuron models.

本文提出了一种基于cmos的神经元网络，可以模拟生物神经振荡器模型中的自振荡偏置行为。基于LIF （leaky integrative -and-fire）神经元模型，本文提出的神经元回路采用互反抑制网络和突触疲劳的概念以及兴奋性驱动刺激来复制膜电位的胞外流体偏倚。在基础神经元电路的顶部，一个激励积分器集成正、负兴奋输入尖峰来刺激膜电位偏置，一个偏置控制器接收抑制驱动输入并根据膜电位偏置水平产生输出抑制驱动。所提出的具有抑制性连接的多个神经元网络可以产生振荡膜电位偏差，这可以用作神经元尖峰放电的局部动态阈值，从而产生自模式输出尖峰，如开关或动态放电速率模式。该神经元网络采用250 nm CMOS工艺实现，工作电压为2.5 V，全工作时每个神经元平均功耗为99.31μW。测量了不同输入条件下的工作波形，可以产生多种输出模式。为了验证操作的稳定性，我们从32个神经元中测量了由于过程变化而导致的输出信号的方差，显示每个输入尖峰的膜电位增益的标准差为18%，膜电位偏置的振荡周期的标准差为12%。结果表明，所提出的神经元网络能够复制生物神经元模型的自振荡行为。

{"title":"CMOS LIF Neurons With Local Membrane Dynamic Biasing Based on Reciprocal Inhibition for Self-Oscillatory Neural Networks","authors":"Mannhee Cho;Minil Kang;Minseong Um;Hangue Park;Hyung-Min Lee","doi":"10.1109/TBCAS.2025.3583093","DOIUrl":"10.1109/TBCAS.2025.3583093","url":null,"abstract":"This paper presents a CMOS-based neuron network that can emulate self-oscillatory biasing behaviors found in biological neural oscillator models. Based on leaky integrate-and-fire (LIF) neuron models, the proposed neuron circuit adopts the concept of reciprocal inhibitory network and synaptic fatigue as well as excitatory drive stimulation for replicating extracellular fluidic biasing of membrane potentials. On top of the base neuron circuit, an excitation integrator integrates positive and negative excitatory input spikes to stimulate the membrane potential bias, and a bias controller receives inhibitory drive input and generates output inhibitory drives depending on the membrane potential bias level. The proposed networks of multiple neurons with inhibitory connections can generate oscillating membrane potential biases, which can be used as local dynamic thresholds for neuron spike firing, resulting in self-patterned output spikes such as switching or dynamic firing rate patterns. The proposed neuron network was implemented with 250-nm CMOS process operating at the supply voltage of 2.5 V and consuming average power of 99.31 <inline-formula><tex-math>$boldsymbol{mu}$</tex-math></inline-formula>W per neuron during full operation. Operation waveforms were measured in various input conditions which can produce multiple output patterns. Variances in output signals due to process variation were measured from 32 neurons to verify the stability of operation, showing the standard deviation of 18% in the membrane potential gain per input spike and 12% in oscillation periods of the membrane potential bias. The results verified that the proposed neuron network can replicate the self-oscillatory behaviors of biological neuron models.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 6","pages":"1213-1225"},"PeriodicalIF":4.9,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0