IEEE transactions on biomedical circuits and systems最新文献

{"title":"An 800MΩ-Input-Impedance 95.3dB-DR Δ-ΔΣ AFE for Dry-Electrode Wearable EEG Recording","authors":"Yuying Li;Yijie Li;Hao Li;Zhiliang Hong;Jiawei Xu","doi":"10.1109/TBCAS.2024.3374891","DOIUrl":"10.1109/TBCAS.2024.3374891","url":null,"abstract":"Non-invasive, closed-loop brain modulation offers an accessible and cost-effective means of evaluating and modulating one’s mental and physical well-being, such as Parkinson’s disease, epilepsy, and sleep disorders. However, wearable EEG systems pose significant challenges for the analog front-end (AFE) circuits in view of µV-level EEG signals of interest, multiple sources of interference, and ill-defined skin contact. This paper presents a direct-digitization AFE tailored for dry-electrode scalp EEG recording, characterized by wide input dynamic range (DR) and high input impedance. The AFE utilizes a second-order 5-bit delta-delta sigma (Δ-ΔΣ) ADC to shape DC electrode offset (DEO) and low-frequency disturbances while retaining high accuracy. A non-inverting pseudo-differential instrumentation amplifier (IA) embedded in the ADC ensures high input impedance (Z\u0000_in\u0000) and common-mode rejection ratio (CMRR). Fabricated in a standard 0.18-μm CMOS process, the AFE delivers 700-mV\u0000_pp\u0000 input signal range, 95.3-dB DR, 87-dB SNDR, and 800-MΩ input impedance at 50 Hz while consuming 88.4µW from a 1.2 V supply. The benefits of high DR and high input impedance have been validated by dry-electrode EEG measurement.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 5","pages":"1079-1088"},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140066384","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}

{"title":"An Energy-Efficient FD-fNIRS Readout Circuit Employing a Mixer-First Analog Frontend and a $Sigma$-$Delta$ Phase-to-Digital Converter","authors":"Zhouchen Ma;Cheng Chen;Yuxiang Lin;Liang Qi;Yongfu Li;Xia Bi;Mohamad Sawan;Guoxing Wang;Jian Zhao","doi":"10.1109/TBCAS.2024.3372887","DOIUrl":"10.1109/TBCAS.2024.3372887","url":null,"abstract":"This paper presents a low-power frequency-domain functional near-infrared spectroscopy (FD-fNIRS) readout circuit for the absolute value measurement of tissue optical characteristics. The paper proposes a mixer-first analog front-end (AFE) structure and a 1-bit \u0000<inline-formula><tex-math>$Sigma$</tex-math></inline-formula>\u0000-\u0000<inline-formula><tex-math>$Delta$</tex-math></inline-formula>\u0000 phase-to-digital converter (PDC) to reduce the required circuit bandwidth and the laser modulation frequency, thereby saving power while maintaining high resolution. The proposed chip achieves sub-0.01\u0000<inline-formula><tex-math>${}^{circ}$</tex-math></inline-formula>\u0000 phase resolution and consumes 6.8 mW of power. Nine optical solid phantoms are produced to evaluate the chip. Compared to a self-built high-precision measurement platform that combines a network analyzer with an avalanche photodiode (APD) module, the maximum measuring errors of the absorption coefficient and reduced scattering coefficient are 10.6% and 12.3%, respectively.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 4","pages":"938-950"},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140029817","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}

{"title":"A Wirelessly Powered Scattered Neural Recording Wearable System","authors":"Yiming Han;Linran Zhao;Raymond G. Stephany;Ju-Chun Hsieh;Huiliang Wang;Yaoyao Jia","doi":"10.1109/TBCAS.2024.3397669","DOIUrl":"10.1109/TBCAS.2024.3397669","url":null,"abstract":"This paper introduces a wirelessly powered scattered neural recording wearable system that can facilitate continuous, untethered, and long-term electroencephalogram (EEG) recording. The proposed system, including 32 standalone EEG recording devices and a central controller, is incorporated in a wearable form factor. The standalone devices are sparsely distributed on the scalp, allowing for flexible placement and varying quantities to provide extensive spatial coverage and scalability. Each standalone device featuring a low-power EEG recording application-specific integrated circuit (ASIC) wirelessly receives power through a 60 MHz inductive link. The low-power ASIC design (84.6 µW) ensures sufficient wireless power reception through a small receiver (Rx) coil. The 60 MHz inductive link also serves as the data carrier for wireless communication between standalone devices and the central controller, eliminating the need for additional data antennas. All these efforts contribute to the miniaturization of standalone devices with dimensions of 12 × 12 × 5 mm\u0000³\u0000, enhancing device wearability. The central controller applies the pulse width modulation (PWM) scheme on the 60 MHz carrier, transmitting user commands at 4 Mbps to EEG recording ASICs. The ASIC employs a novel synchronized PWM demodulator to extract user commands, operating signal digitization and data transmission. The analog frontend (AFE) amplifies the EEG signal with a gain of 45 dB and applies band-pass filtering from 0.03 Hz to 400 Hz, with an input-referred noise (IRN) of 3.62 µV\u0000_RMS\u0000. The amplified EEG signal is then digitized by a 10-bit successive approximation register (SAR) analog-to-digital converter (ADC) with a peak signal-to-noise and distortion ratio (SNDR) of 55.4 dB. The resulting EEG data is transmitted to an external software-defined radio (SDR) Rx through load-shift-keying (LSK) backscatter at 3.75 Mbps. The system’s functionality is fully evaluated in human experiments.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 4","pages":"734-745"},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140878222","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}

{"title":"A Wireless Multimodal Physiological Monitoring ASIC for Animal Health Monitoring Injectable Devices","authors":"Linran Zhao;Raymond G. Stephany;Yiming Han;Parvez Ahmmed;Tzu-Ping Huang;Alper Bozkurt;Yaoyao Jia","doi":"10.1109/TBCAS.2024.3372571","DOIUrl":"10.1109/TBCAS.2024.3372571","url":null,"abstract":"Utilizing injectable devices for monitoring animal health offers several advantages over traditional wearable devices, including improved signal-to-noise ratio (SNR) and enhanced immunity to motion artifacts. We present a wireless application-specific integrated circuit (ASIC) for injectable devices. The ASIC has multiple physiological sensing modalities including body temperature monitoring, electrocardiography (ECG), and photoplethysmography (PPG). The ASIC fabricated using the CMOS 180 nm process is sized to fit into an injectable microchip implant. The ASIC features a low-power design, drawing an average DC power of 155.3 µW, enabling the ASIC to be wirelessly powered through an inductive link. To capture the ECG signal, we designed the ECG analog frontend (AFE) with 0.3 Hz low cut-off frequency and 45-79 dB adjustable midband gain. To measure PPG, we employ an energy-efficient and safe switched-capacitor-based (SC) light emitting diode (LED) driver to illuminate an LED with milliampere-level current pulses. A SC integrator-based AFE converts the current of photodiode with a programmable transimpedance gain. A resistor-based Wheatstone Bridge (WhB) temperature sensor followed by an instrumentation amplifier (IA) provides 27–47 °C sensing range with 0.02 °C inaccuracy. Recorded physiological signals are sequentially sampled and quantized by a 10-bit analog-to-digital converter (ADC) with the successive approximation register (SAR) architecture. The SAR ADC features an energy-efficient switching scheme and achieves a 57.5 dB signal-to-noise-and-distortion ratio (SNDR) within 1 kHz bandwidth. Then, a back data telemetry transmits the baseband data via a backscatter scheme with intermediate-frequency assistance. The ASIC’s overall functionality and performance has been evaluated through an \u0000<italic>in vivo</i>\u0000 experiment.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 5","pages":"1037-1049"},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140029816","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}

{"title":"A Spatially Diverse 2TX-3RX Galvanic-Coupled Transdural Telemetry for Tether-Less Distributed Brain–Computer Interfaces","authors":"Chengyao Shi;Yuming He;Marios Gourdouparis;Guido Dolmans;Yao-Hong Liu","doi":"10.1109/TBCAS.2024.3373172","DOIUrl":"10.1109/TBCAS.2024.3373172","url":null,"abstract":"A near-field galvanic coupled transdural telemetry ASICs for intracortical brain-computer interfaces is presented. The proposed design features a two channels transmitter and three channels receiver (2TX-3RX) topology, which introduces spatial diversity to effectively mitigate misalignments (both lateral and rotational) between the brain and the skull and recovers the path loss by 13 dB when the RX is in the worst-case blind spot. This spatial diversity also allows the presented telemetry to support the spatial division multiplexing required for a high-capacity multi-implant distributed network. It achieves a signal-to-interference ratio of 12 dB, even with the adjacent interference node placed only 8 mm away from the desired link. While consuming only 0.33 mW for each channel, the presented RX achieves a wide bandwidth of 360 MHz and a low input referred noise of 13.21 nV/√\u0000<italic>H</i>\u0000z. The presented telemetry achieves a 270 Mbps data rate with a BER < 10\u0000⁻⁶\u0000 and an energy efficiency of 3.4 pJ/b and 3.7 pJ/b, respectively. The core footprint of the TX and RX modules is only 100 and 52 mm\u0000²\u0000, respectively, minimizing the invasiveness of the surgery. The proposed transdural telemetry system has been characterized ex-vivo with a 7-mm thick porcine tissue.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 5","pages":"1014-1023"},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140029815","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}

{"title":"A Reconfigurable Near-Sensor Processor for Anomaly Detection in Limb Prostheses","authors":"Jiayu Huang;Zikai Zhu;Peng Su;Dejiu Chen;Li-Rong Zheng;Zhuo Zou","doi":"10.1109/TBCAS.2024.3370571","DOIUrl":"10.1109/TBCAS.2024.3370571","url":null,"abstract":"This paper presents a reconfigurable near-sensor anomaly detection processor to real-time monitor the potential anomalous behaviors of amputees with limb prostheses. The processor is low-power, low-latency, and suitable for equipment on the prostheses and comprises a reconfigurable Variational Autoencoder (VAE), a scalable Self-Organizing Map (SOM) Array, and a window-size-adjustable Markov Chain, which can implement an integrated miniaturized anomaly detection system. With the reconfigurable VAE, the proposed processor can support up to 64 sensor sampling channels programmable by global configuration, which can meet the anomaly detection requirements in different scenarios. A scalable SOM array allows for the selection of different sizes based on the complexity of the data. Unlike traditional time accumulation-based anomaly detection methods, the Markov Chain is utilized to detect time-series-based anomalous data. The processor is designed and fabricated in a UMC 40-nm LP technology with a core area of 1.49 mm\u0000<inline-formula><tex-math>${}^{2}$</tex-math></inline-formula>\u0000 and a power consumption of 1.81 mW. It achieves real-time detection performance with 0.933 average F1 Score for the FSP dataset within 24.22 \u0000<inline-formula><tex-math>$mu$</tex-math></inline-formula>\u0000s, and 0.956 average F1 Score for the SFDLA-12 dataset within 30.48 \u0000<inline-formula><tex-math>$mu$</tex-math></inline-formula>\u0000s. The energy dissipation of detection for each input feature is 43.84 nJ with the FSP dataset, and 55.17 nJ with the SFDLA-12 dataset. Compared with ARM Cortex-M4 and ARM Cortex-M33 microcontrollers, the processor achieves energy and area efficiency improvements ranging from 257\u0000<inline-formula><tex-math>$boldsymbol{times}$</tex-math></inline-formula>\u0000, 193\u0000<inline-formula><tex-math>$boldsymbol{times}$</tex-math></inline-formula>\u0000 and 11\u0000<inline-formula><tex-math>$boldsymbol{times}$</tex-math></inline-formula>\u0000, 8\u0000<inline-formula><tex-math>$boldsymbol{times}$</tex-math></inline-formula>\u0000, respectively.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 5","pages":"976-989"},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139992184","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}

{"title":"A Low-Noise Low-Power 0.001Hz–1kHz Neural Recording System-on-Chip With Sample-Level Duty-Cycling","authors":"Jiajia Wu;Abraham Akinin;Jonathan Somayajulu;Min S. Lee;Akshay Paul;Hongyu Lu;Yongjae Park;Seong-Jin Kim;Patrick P. Mercier;Gert Cauwenberghs","doi":"10.1109/TBCAS.2024.3368068","DOIUrl":"10.1109/TBCAS.2024.3368068","url":null,"abstract":"Advances in brain-machine interfaces and wearable biomedical sensors for healthcare and human-computer interactions call for precision electrophysiology to resolve a variety of biopotential signals across the body that cover a wide range of frequencies, from the mHz-range electrogastrogram (EGG) to the kHz-range electroneurogram (ENG). Existing integrated wearable solutions for minimally invasive biopotential recordings are limited in detection range and accuracy due to trade-offs in bandwidth, noise, input impedance, and power consumption. This article presents a 16-channel wide-band ultra-low-noise neural recording system-on-chip (SoC) fabricated in 65nm CMOS for chronic use in mobile healthcare settings that spans a bandwidth of 0.001 Hz to 1 kHz through a featured sample-level duty-cycling (SLDC) mode. Each recording channel is implemented by a delta-sigma analog-to-digital converter (ADC) achieving 1.0 \u0000<inline-formula><tex-math>$mu$</tex-math></inline-formula>\u0000 V\u0000<inline-formula><tex-math>${}_{rms}$</tex-math></inline-formula>\u0000 input-referred noise over 1Hz–1kHz bandwidth with a Noise Efficiency Factor (NEF) of 2.93 in continuous operation mode. In SLDC mode, the power supply is duty-cycled while maintaining consistently low input-referred noise levels at ultra-low frequencies (1.1\u0000<inline-formula><tex-math>$mu$</tex-math></inline-formula>\u0000V\u0000<inline-formula><tex-math>${}_{rms}$</tex-math></inline-formula>\u0000 over 0.001Hz–1Hz) and 435 M\u0000<inline-formula><tex-math>$Omega$</tex-math></inline-formula>\u0000 input impedance. The functionalities of the proposed SoC are validated with two human electrophysiology applications: recording low-amplitude electroencephalogram (EEG) through electrodes fixated on the forehead to monitor brain waves, and ultra-slow-wave electrogastrogram (EGG) through electrodes fixated on the abdomen to monitor digestion.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 2","pages":"263-273"},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139975132","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}

{"title":"A Bioimpedance Spectroscopy Interface for EIM Based on IF-Sampling and Pseudo 2-Path SC Bandpass $DeltaSigma$ ADC","authors":"Alejandro D. Fernandez Schrunder;Yu-Kai Huang;Saul Rodriguez;Ana Rusu","doi":"10.1109/TBCAS.2024.3370399","DOIUrl":"10.1109/TBCAS.2024.3370399","url":null,"abstract":"This paper presents a low-noise bioimpedance (bio-Z) spectroscopy interface for electrical impedance myography (EIM) over the 1 kHz to 2 MHz frequency range. The proposed interface employs a sinusoidal signal generator based on direct-digital-synthesis (DDS) to improve the accuracy of the bio-Z reading, and a quadrature low-intermediate frequency (IF) readout to achieve a good noise-to-power efficiency and the required data throughput to detect muscle contractions. The readout is able to measure baseline and time-varying bio-Z by employing robust and power-efficient low-gain IAs and sixth-order single-bit bandpass (BP) \u0000<inline-formula><tex-math>$mathbf{DeltaSigma}$</tex-math></inline-formula>\u0000 ADCs. The proposed bio-Z spectroscopy interface is implemented in a 180 nm CMOS process, consumes 344.3 \u0000<inline-formula><tex-math>$-$</tex-math></inline-formula>\u0000 479.3 \u0000<inline-formula><tex-math>$mathbf{mu}$</tex-math></inline-formula>\u0000W, and occupies 5.4 mm\u0000²\u0000 area. Measurement results show 0.7 m\u0000<inline-formula><tex-math>$mathbf{Omega}$</tex-math></inline-formula>\u0000/\u0000<inline-formula><tex-math>$sqrt{text{Hz}}$</tex-math></inline-formula>\u0000 sensitivity at 15.625 kHz, 105.8 dB SNR within 4 Hz bandwidth, and a 146.5 dB figure-of-merit. Additionally, recording of EIM in time and frequency domain during contractions of the bicep brachii muscle demonstrates the potential of the proposed bio-Z interface for wearable EIM systems.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 5","pages":"1001-1013"},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10445383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139975131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GNN-Based Concentration Prediction With Variable Input Flow Rates for Microfluidic Mixers","authors":"Weiqing Ji;Xingzhuo Guo;Shouan Pan;Fei Long;Tsung-Yi Ho;Ulf Schlichtmann;Hailong Yao","doi":"10.1109/TBCAS.2024.3366691","DOIUrl":"10.1109/TBCAS.2024.3366691","url":null,"abstract":"Recent years have witnessed significant advances brought by microfluidic biochips in automating biochemical protocols. Accurate preparation of fluid samples is an essential component of these protocols, where concentration prediction and generation are critical. Equipped with the advantages of convenient fabrication and control, microfluidic mixers demonstrate huge potential in sample preparation. Although finite element analysis (FEA) is the most commonly used simulation method for accurate concentration prediction of a given microfluidic mixer, it is time-consuming with poor scalability for large biochip sizes. Recently, machine learning models have been adopted in concentration prediction, with great potential in enhancing the efficiency over traditional FEA methods. However, the state-of-the-art machine learning-based method can only predict the concentration of mixers with fixed input flow rates and fixed sizes. In this paper, we propose a new concentration prediction method based on graph neural networks (GNNs), which can predict output concentrations for microfluidic mixters with variable input flow rates. Moreover, a transfer learning method is proposed to transfer the trained model to mixers of different sizes with reduced training data. Experimental results show that, for microfluidic mixers with fixed input flow rates, the proposed method obtains an average reduction of 88% in terms of prediction errors compared with the state-of-the-art method. For microfluidic mixers with variable input flow rates, the proposed method reduces the prediction error by 85% on average. Besides, the proposed transfer learning method reduces the training data by 84% for extending the pre-trained model for microfluidic mixers of different sizes with acceptable prediction error.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 3","pages":"622-635"},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139941414","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}

{"title":"High Sensitivity and High Throughput Magnetic Flow CMOS Cytometers With 2D Oscillator Array and Inter-Sensor Spectrogram Cross-Correlation","authors":"Hao Tang;Suresh Venkatesh;Zhongtian Lin;Xuyang Lu;Hooman Saeeidi;Mehdi Javanmard;Kaushik Sengupta","doi":"10.1109/TBCAS.2024.3367668","DOIUrl":"10.1109/TBCAS.2024.3367668","url":null,"abstract":"In the paper, we present an integrated flow cytometer with a 2D array of magnetic sensors based on dual-frequency oscillators in a 65-nm CMOS process, with the chip packaged with microfluidic controls. The sensor architecture and the presented array signal processing allows uninhibited flow of the sample for high throughput without the need for hydrodynamic focusing to a single sensor. To overcome the challenge of sensitivity and specificity that comes as a trade off with high throughout, we perform two levels of signal processing. First, utilizing the fact that a magnetically tagged cell is expected to excite sequentially an array of sensors in a time-delayed fashion, we perform inter-site cross-correlation of the sensor spectrograms that allows us to suppress the probability of false detection drastically, allowing theoretical sensitivity reaching towards sub-ppM levels that is needed for rare cell or circulating tumor cell detection. In addition, we implement two distinct methods to suppress correlated low frequency drifts of singular sensors—one with an on-chip sensor reference and one that utilizes the frequency dependence of the susceptibility of super-paramagnetic magnetic beads that we deploy as tags. We demonstrate these techniques on a 7\u0000<inline-formula><tex-math>$times$</tex-math></inline-formula>\u00007 sensor array in 65 nm CMOS technology packaged with microfluidics with magnetically tagged dielectric particles and cultu lymphoma cancer cells.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"18 4","pages":"923-937"},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139941415","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}