Pub Date : 2025-03-09DOI: 10.1109/TBCAS.2025.3568754
Asish Koruprolu;Tyler Hack;Omid Ghadami;Aditi Jain;Drew A. Hall
Continuous health monitoring by placing sensors onto and into the human body has emerged as a pivotal approach in healthcare. This paper delves into the vast array of opportunities presented by instrumenting the body using wearable, ingestible, injectable, and implantable sensors. These sensors enable the continuous monitoring of vital signs, biomarkers, and other crucial health metrics, thus assessing an individual’s physiological state. This comprehensive health data empowers healthcare providers and individuals alike to make informed decisions and take timely action. Moreover, integrating sensors into the human body enables personalized medicine, enhances disease detection and management, and offers possibilities for proactive health interventions and preventive care to improve overall well-being.
{"title":"From Wearables to Implantables: Harnessing Sensor Technologies for Continuous Health Monitoring","authors":"Asish Koruprolu;Tyler Hack;Omid Ghadami;Aditi Jain;Drew A. Hall","doi":"10.1109/TBCAS.2025.3568754","DOIUrl":"10.1109/TBCAS.2025.3568754","url":null,"abstract":"Continuous health monitoring by placing sensors onto and into the human body has emerged as a pivotal approach in healthcare. This paper delves into the vast array of opportunities presented by instrumenting the body using wearable, ingestible, injectable, and implantable sensors. These sensors enable the continuous monitoring of vital signs, biomarkers, and other crucial health metrics, thus assessing an individual’s physiological state. This comprehensive health data empowers healthcare providers and individuals alike to make informed decisions and take timely action. Moreover, integrating sensors into the human body enables personalized medicine, enhances disease detection and management, and offers possibilities for proactive health interventions and preventive care to improve overall well-being.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 5","pages":"852-875"},"PeriodicalIF":4.9,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144029207","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}
In the practical implementations of wearable sensors, motion artifacts with large amplitudes often cause signal chain saturation, significantly degrading biopotential signal integrity. Similarly, rapid stimulation artifacts are inevitable during closed-loop brain stimulation therapy, posing additional challenges for real-time signal acquisition. To address motion and stimulation artifacts with amplitudes reaching hundreds of mV while minimizing information loss, a sensor interface with high input range and fast artifacts recovery capability is essential. This paper presents a continuous-time track-and-zoom (CT-TAZ) technique designed to handle large artifacts events without saturation. The proposed system achieves a 3.6V/1.8V differential-mode/common-mode full-scale input range. Fabricated in a 180nm CMOS process, the prototype chip occupies an area of 0.694mm2 and consumes 12/32.6/51.6$mu$W for recordings without/with single-end/with differential rail-to-rail artifacts. The system demonstrates an average artifacts recovery time of 65.3 $mu$s under 3.6V stimulation artifacts, achieving an average artifacts recovery speed of 52.3mVpp/μs, which is 2.25× larger input range and 3× faster recovery compared to the state-of-the-art.
{"title":"Artifact-Tolerant Electrophysiological Sensor Interface With 3.6V/1.8V DM/CM Input Range and 52.3mVpp/${mu}$s Recovery Using Asynchronous Signal Folding","authors":"Qiao Cai;Xinzi Xu;Yanxing Suo;Guanghua Qian;Yongfu Li;Guoxing Wang;Yong Lian;Yang Zhao","doi":"10.1109/TBCAS.2025.3567524","DOIUrl":"10.1109/TBCAS.2025.3567524","url":null,"abstract":"In the practical implementations of wearable sensors, motion artifacts with large amplitudes often cause signal chain saturation, significantly degrading biopotential signal integrity. Similarly, rapid stimulation artifacts are inevitable during closed-loop brain stimulation therapy, posing additional challenges for real-time signal acquisition. To address motion and stimulation artifacts with amplitudes reaching hundreds of mV while minimizing information loss, a sensor interface with high input range and fast artifacts recovery capability is essential. This paper presents a continuous-time track-and-zoom (CT-TAZ) technique designed to handle large artifacts events without saturation. The proposed system achieves a 3.6V/1.8V differential-mode/common-mode full-scale input range. Fabricated in a 180nm CMOS process, the prototype chip occupies an area of 0.694mm<sup>2</sup> and consumes 12/32.6/51.6<inline-formula><tex-math>$mu$</tex-math></inline-formula>W for recordings without/with single-end/with differential rail-to-rail artifacts. The system demonstrates an average artifacts recovery time of 65.3 <inline-formula><tex-math>$mu$</tex-math></inline-formula>s under 3.6V stimulation artifacts, achieving an average artifacts recovery speed of 52.3mV<sub>pp</sub>/μs, which is 2.25× larger input range and 3× faster recovery compared to the state-of-the-art.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 6","pages":"1160-1174"},"PeriodicalIF":4.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143995414","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}
Pub Date : 2025-02-11DOI: 10.1109/TBCAS.2025.3538049
{"title":"IEEE Circuits and Systems Society Information","authors":"","doi":"10.1109/TBCAS.2025.3538049","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3538049","url":null,"abstract":"","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 1","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10880491","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388582","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}
Pub Date : 2025-02-11DOI: 10.1109/TBCAS.2025.3538047
{"title":"IEEE Transactions on Biomedical Circuits and Systems Publication Information","authors":"","doi":"10.1109/TBCAS.2025.3538047","DOIUrl":"https://doi.org/10.1109/TBCAS.2025.3538047","url":null,"abstract":"","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 1","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10880493","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388576","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}
Pub Date : 2025-02-11DOI: 10.1109/TBCAS.2024.3411913
Jing Liang;Yuanqi Hu
In [1], in section III.E of the article, we calculate the equivalent tunnelling current according to equation (4) by using the value of Cg, eff as 1.679 fF, which is about 4.6 times smaller than the correct value. This leads to the wrong equivalent impedance value obtained in the final Fig. 10 is about 4.6 times larger than the correct value, and the equivalent impedance should be about 2.2 PΩ at this size, so according to the basis of the above, the article should be corrected as follows:
{"title":"Erratum to “Design of an Extreme Low Cutoff Frequency Highpass Frontend for CMOS ISFET via Direct Tunneling Principle”","authors":"Jing Liang;Yuanqi Hu","doi":"10.1109/TBCAS.2024.3411913","DOIUrl":"https://doi.org/10.1109/TBCAS.2024.3411913","url":null,"abstract":"In [1], in section III.E of the article, we calculate the equivalent tunnelling current according to equation (4) by using the value of Cg, eff as 1.679 fF, which is about 4.6 times smaller than the correct value. This leads to the wrong equivalent impedance value obtained in the final Fig. 10 is about 4.6 times larger than the correct value, and the equivalent impedance should be about 2.2 PΩ at this size, so according to the basis of the above, the article should be corrected as follows:","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 1","pages":"238-238"},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10880511","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388553","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}
The miniaturization and real time imaging capability have always been the desired properties of photoacoustic imaging (PAI) system, which unlocked vast potential for personalized healthcare and diagnostics. While the imaging quality and resolution in such systems are inferior due to physics and system volume constraints, which limited its wide deployment and application. This paper proposes a novel platform to enhance the imaging quality of handheld PAI system in real time, integrating MultiResU-Net imaging enhancement algorithm with Ferroelectric random-access memory (FeRAM) crossbar array. The FeRAM crossbar array enables in memory computing, which is highly suitable for accelerating deep neural network where extensive matrix multiplications are involved. The hardware implementation of the algorithm is optimized for low-power operation on edge devices, a specifically designed algorithmic strategy is further introduced to accurately simulate the impact of hardware variation on the computation in the array with time complexity of O(mn). The feasibility and effectiveness of this method are demonstrated through simulation data (synthesized through physical model) and in vivo data, the experimental results demonstrate more than 10 times of imaging resolution improvement. The execution of neural network inference has been significantly accelerated and can be completed within a few microseconds, fully covering the imaging speed in handheld PAI system and satisfying the real time imaging capability. The whole platform can be integrated into a compact size of 25$times$25$times$20 cm3, which is a portable system with real time and high resolution imaging capability.
{"title":"Real-Time Imaging Enhancement of Handheld Photoacoustic System With FeRAM Crossbar Array Based Neuromorphic Design","authors":"Zhengyuan Zhang;Tiancheng Cao;Siyu Liu;Haoran Jin;Wensong Wang;Xiangjun Yin;Chen Liu;Wang Ling Goh;Yuan Gao;Yuanjin Zheng","doi":"10.1109/TBCAS.2025.3538578","DOIUrl":"10.1109/TBCAS.2025.3538578","url":null,"abstract":"The miniaturization and real time imaging capability have always been the desired properties of photoacoustic imaging (PAI) system, which unlocked vast potential for personalized healthcare and diagnostics. While the imaging quality and resolution in such systems are inferior due to physics and system volume constraints, which limited its wide deployment and application. This paper proposes a novel platform to enhance the imaging quality of handheld PAI system in real time, integrating MultiResU-Net imaging enhancement algorithm with Ferroelectric random-access memory (FeRAM) crossbar array. The FeRAM crossbar array enables in memory computing, which is highly suitable for accelerating deep neural network where extensive matrix multiplications are involved. The hardware implementation of the algorithm is optimized for low-power operation on edge devices, a specifically designed algorithmic strategy is further introduced to accurately simulate the impact of hardware variation on the computation in the array with time complexity of <italic>O(mn)</i>. The feasibility and effectiveness of this method are demonstrated through simulation data (synthesized through physical model) and <italic>in vivo</i> data, the experimental results demonstrate more than 10 times of imaging resolution improvement. The execution of neural network inference has been significantly accelerated and can be completed within a few microseconds, fully covering the imaging speed in handheld PAI system and satisfying the real time imaging capability. The whole platform can be integrated into a compact size of 25<inline-formula><tex-math>$times$</tex-math></inline-formula>25<inline-formula><tex-math>$times$</tex-math></inline-formula>20 <bold>cm<sup>3</sup></b>, which is a portable system with real time and high resolution imaging capability.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 5","pages":"1031-1044"},"PeriodicalIF":4.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143545248","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}
Pub Date : 2025-01-27DOI: 10.1109/TBCAS.2025.3533612
Maryam Habibollahi;Dai Jiang;Henry Thomas Lancashire;Andreas Demosthenous
A mm-sized, implantable neural interface for bidirectional control of the peripheral nerves with microchannel electrodes is presented in this paper. The application-specific integrated circuit (ASIC) developed in a 0.18 $mu$m CMOS technology is designed to achieve highly selective, concurrent control of 300-$mu$m-wide groups of small nerve sections. It has in-situ, high-voltage-compliant (45 V) electrical stimulation and low-voltage (1.8 V) neural recording in each channel. Biphasic stimulus current pulses up to 124 $mu$A, with a 2 $mu$A resolution are generated between 7.4 Hz and 20 kHz frequencies to stimulate and block neural activity. Action potentials are measured across a 10 kHz bandwidth with a variable gain response that ranges up to 72 dB. The neural recording front-end implements a low-power and low-noise biopotential amplifier with an input-referred noise (IRN) of 2.74 $mu$Vrms across the full measurement bandwidth. Automatic detection and reduction of stimulus artifacts is realised using a pole-shifting mechanism with a 1-ms amplifier recovery time. Versatile control of concurrently-operating channels is achieved in a two-channel, 2.31 mm2 interface ASIC using local control that allows up to seven devices to operate in parallel. In vitro validation of the active interface shows feasibility for closed-loop peripheral nerve control, while ex vivo analyses of concurrent stimulation and recording demonstrates the measured neural response to electrical stimuli.
{"title":"An Active Microchannel Neural Interface for Implantable Electrical Stimulation and Recording","authors":"Maryam Habibollahi;Dai Jiang;Henry Thomas Lancashire;Andreas Demosthenous","doi":"10.1109/TBCAS.2025.3533612","DOIUrl":"10.1109/TBCAS.2025.3533612","url":null,"abstract":"A mm-sized, implantable neural interface for bidirectional control of the peripheral nerves with microchannel electrodes is presented in this paper. The application-specific integrated circuit (ASIC) developed in a 0.18 <inline-formula><tex-math>$mu$</tex-math></inline-formula>m CMOS technology is designed to achieve highly selective, concurrent control of 300-<inline-formula><tex-math>$mu$</tex-math></inline-formula>m-wide groups of small nerve sections. It has <italic>in-situ</i>, high-voltage-compliant (45 V) electrical stimulation and low-voltage (1.8 V) neural recording in each channel. Biphasic stimulus current pulses up to 124 <inline-formula><tex-math>$mu$</tex-math></inline-formula>A, with a 2 <inline-formula><tex-math>$mu$</tex-math></inline-formula>A resolution are generated between 7.4 Hz and 20 kHz frequencies to stimulate and block neural activity. Action potentials are measured across a 10 kHz bandwidth with a variable gain response that ranges up to 72 dB. The neural recording front-end implements a low-power and low-noise biopotential amplifier with an input-referred noise (IRN) of 2.74 <inline-formula><tex-math>$mu$</tex-math></inline-formula>V<sub>rms</sub> across the full measurement bandwidth. Automatic detection and reduction of stimulus artifacts is realised using a pole-shifting mechanism with a 1-ms amplifier recovery time. Versatile control of concurrently-operating channels is achieved in a two-channel, 2.31 mm<sup>2</sup> interface ASIC using local control that allows up to seven devices to operate in parallel. <italic>In vitro</i> validation of the active interface shows feasibility for closed-loop peripheral nerve control, while <italic>ex vivo</i> analyses of concurrent stimulation and recording demonstrates the measured neural response to electrical stimuli.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 5","pages":"1018-1030"},"PeriodicalIF":4.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143545027","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}
Pub Date : 2025-01-22DOI: 10.1109/TBCAS.2025.3532465
Roman Willaredt;Christoph Grandauer;Daniel De Dorigo;Daniel Wendler;Matthias Kuhl;Yiannos Manoli
Host connectivity for invasive, high-density neural probes that integrate all the circuits needed for in-situ digitization of brain activity in the shank requires a thin and conformal cable. To minimize tissue damage during insertion or from micro-movements during chronic use, the wiring must be constrained in size with a low number of interconnects. Reducing the number of traces results in thinner and more flexible cables and allows the data rate to be increased by using wider traces. Fewer contacts are also less susceptible to reliability issues in long-term applications. This paper presents a modular digital neural probe that embeds a two-wire bidirectional interface for host connectivity minimizing the data overhead for configuration and readout. The presented handshaking allows synchronization of multiple shanks and is designed to adapt to varying line delays caused by different cable lengths or changing environmental conditions. Data reduction based on delta encoding further increases the number of electrodes that can be read out simultaneously. The system is validated in a 192-channel neural probe fabricated in a 180 nm CMOS technology with a supply voltage of 1.2 V.
{"title":"Compact Low-Power Interfacing and Data Reduction for Floating Active Intracortical Neural Probes With Modular Architecture","authors":"Roman Willaredt;Christoph Grandauer;Daniel De Dorigo;Daniel Wendler;Matthias Kuhl;Yiannos Manoli","doi":"10.1109/TBCAS.2025.3532465","DOIUrl":"10.1109/TBCAS.2025.3532465","url":null,"abstract":"Host connectivity for invasive, high-density neural probes that integrate all the circuits needed for in-situ digitization of brain activity in the shank requires a thin and conformal cable. To minimize tissue damage during insertion or from micro-movements during chronic use, the wiring must be constrained in size with a low number of interconnects. Reducing the number of traces results in thinner and more flexible cables and allows the data rate to be increased by using wider traces. Fewer contacts are also less susceptible to reliability issues in long-term applications. This paper presents a modular digital neural probe that embeds a two-wire bidirectional interface for host connectivity minimizing the data overhead for configuration and readout. The presented handshaking allows synchronization of multiple shanks and is designed to adapt to varying line delays caused by different cable lengths or changing environmental conditions. Data reduction based on delta encoding further increases the number of electrodes that can be read out simultaneously. The system is validated in a 192-channel neural probe fabricated in a 180 nm CMOS technology with a supply voltage of 1.2 V.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 2","pages":"270-279"},"PeriodicalIF":0.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143545114","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}
Pub Date : 2025-01-21DOI: 10.1109/TBCAS.2025.3531995
Asif Iftekhar Omi;Anyu Jiang;Baibhab Chatterjee
In the context of implantable bioelectronics, this work provides new insights into maximizing biomedical wireless power transfer (BWPT) via the systematic development of inductive links. This approach addresses the specific challenges of power transfer efficiency (PTE) optimization within the spatial/area constraints of bio-implants embedded in tissue. Key contributions include the derivation of an optimal self-inductance with S-parameter-based analyses leading to the co-design of planar spiral coils and L-section impedance matching networks. To validate the proposed design methodology, two coil prototypes— one symmetric (type-1) and one asymmetric (type-2)— were fabricated and tested for PTE in pork tissue. Targeting a 20 MHz design frequency, the type-1 coil demonstrated a state-of-the-art PTE of $sim$ 4% (channel length = 15 mm) with a return loss (RL) $>$ 20 dB on both the input and output sides, within an area constraint of $<$ 18$times$18 mm${}^{2}$. In contrast, the type-2 coil achieved a PTE of $sim$ 2% with an RL $>$ 15 dB, for a smaller receiving coil area of $<$ 5$times$5 mm${}^{2}$ for the same tissue environment. To complement the coils, we demonstrate a 65 nm test chip with an integrated energy harvester, which includes a 30-stage rectifier and low-dropout regulator (LDO), producing a stable $sim$ 1V DC output within tissue medium, matching theoretical predictions and simulations. Furthermore, we provide a robust and comprehensive guideline for advancing efficient inductive links for various BWPT applications, with shared resources in GitHub available for utilization by the broader community.
{"title":"Efficient Inductive Link Design: A Systematic Method for Optimum Biomedical Wireless Power Transfer in Area-Constrained Implants","authors":"Asif Iftekhar Omi;Anyu Jiang;Baibhab Chatterjee","doi":"10.1109/TBCAS.2025.3531995","DOIUrl":"10.1109/TBCAS.2025.3531995","url":null,"abstract":"In the context of implantable bioelectronics, this work provides new insights into maximizing biomedical wireless power transfer (BWPT) via the systematic development of inductive links. This approach addresses the specific challenges of power transfer efficiency (PTE) optimization within the spatial/area constraints of bio-implants embedded in tissue. Key contributions include the derivation of an optimal self-inductance with S-parameter-based analyses leading to the co-design of planar spiral coils and L-section impedance matching networks. To validate the proposed design methodology, two coil prototypes— one symmetric (type-1) and one asymmetric (type-2)— were fabricated and tested for PTE in pork tissue. Targeting a 20 MHz design frequency, the type-1 coil demonstrated a state-of-the-art PTE of <inline-formula><tex-math>$sim$</tex-math></inline-formula> 4% (channel length = 15 mm) with a return loss (RL) <inline-formula><tex-math>$>$</tex-math></inline-formula> 20 dB on both the input and output sides, within an area constraint of <inline-formula><tex-math>$<$</tex-math></inline-formula> 18<inline-formula><tex-math>$times$</tex-math></inline-formula>18 mm<inline-formula><tex-math>${}^{2}$</tex-math></inline-formula>. In contrast, the type-2 coil achieved a PTE of <inline-formula><tex-math>$sim$</tex-math></inline-formula> 2% with an RL <inline-formula><tex-math>$>$</tex-math></inline-formula> 15 dB, for a smaller receiving coil area of <inline-formula><tex-math>$<$</tex-math></inline-formula> 5<inline-formula><tex-math>$times$</tex-math></inline-formula>5 mm<inline-formula><tex-math>${}^{2}$</tex-math></inline-formula> for the same tissue environment. To complement the coils, we demonstrate a 65 nm test chip with an integrated energy harvester, which includes a 30-stage rectifier and low-dropout regulator (LDO), producing a stable <inline-formula><tex-math>$sim$</tex-math></inline-formula> 1V DC output within tissue medium, matching theoretical predictions and simulations. Furthermore, we provide a robust and comprehensive guideline for advancing efficient inductive links for various BWPT applications, with shared resources in GitHub available for utilization by the broader community.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":"19 2","pages":"300-316"},"PeriodicalIF":0.0,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143545232","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}
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