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An implantable 700μW 64-channel neuromodulation IC for simultaneous recording and stimulation with rapid artifact recovery
Open accessAbstract
We present an 180nm HV CMOS IC for concurrent neural stimulation and recording that combines 64 low-noise recording front-ends and 4 independent stimulators multiplexed to any of the 64 channels. The stimulators have 5mA peak current, 12V compliance and dynamic power management to maximize efficiency. Co-design of the stimulation and recording subsystems resulted in 100mV of recording linear range, 70nV/rtHz noise, and a rapid 1ms (single-sample) artifact recovery during stimulation.
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“A common solution is to prevent amplifier saturation via a wide linear input range [194].”
From Active Neural Interface Circuits and Systems for Selective Control of Peripheral Nerves: A Review (2024) · cites this paper as [194]
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“Techniques such as adaptive filtering methods rely on the least-mean-squares (LMS) algorithm to implement infinite impulse response (IIR) and finite impulse response (FIR) digital filters to address and cancel out individual samples of artifacts [132], [194].”
From Active Neural Interface Circuits and Systems for Selective Control of Peripheral Nerves: A Review (2024) · cites this paper as [194]
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shulyzki (2015) · IEEE TBioCAS
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A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition
Moin, A., Zhou, A., Rahimi, A., Menon, A., Benatti, S., Alexandrov, G., Tamakloe, S., Ting, J., Yamamoto, N. A. D., Khan, Y., et al. (2020) · Nature Electronics · doi:10.1038/s41928-020-00510-8 · Cited by 698
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A Sub-mm 3 Ultrasonic Free-Floating Implant for Multi-Mote Neural Recording
Ghanbari, M. M., Piech, D. K., Shen, K., Alamouti, S. F., Yalcin, C., Johnson, B. C., Carmena, J. M., Maharbiz, M. M., & Muller, R. (2019) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2019.2936303 · Cited by 137
“When the signal is downmodulated, the kT/C noise is converted into out-of-band chopper ripple [23].”
BackgroundSelf-citationToward true closed-loop neuromodulation: artifact-free recording during stimulation.
Zhou, A., Johnson, B. C., & Muller, R. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.01.012 · Cited by 125
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“2(a)], with a CT M in [21] and a voltagecontrolled oscillator (VCO) ADC in [22].”
BackgroundNason, S. R., Vaskov, A. K., Willsey, M. S., Welle, E. J., An, H., Vu, P. P., Bullard, A. J., Nu, C. S., Kao, J. C., Shenoy, K. V., et al. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-0591-0 · Cited by 113
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“Stimulation artifacts produce rapid and large-amplitude transients in the recorded signals that easily overwhelm the neural response signals, necessitating a paradigm shift in the design of neural recording toward very high input dynamic range and fast transient response [10].”
BackgroundUehlin, J. P., Smith, W. A., Pamula, V. R., Pepin, E. P., Perlmutter, S., Sathe, V., & Rudell, J. C. (2020) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2020.2991524 · Cited by 81
“large voltage swings due to stimulation artifacts [11].”
BackgroundWang, S., López, C. M., Garakoui, S. K., Chun, H., Gómez, D., Sijbers, W., Putzeys, J., Martens, E., Craninckx, J., & Helleputte, N. V. (2019) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2019.2942450 · Cited by 85
Wireless ear EEG to monitor drowsiness
Kaveh, R., Schwendeman, C., Pu, L., Arias, A. C., & Muller, R. (2024) · Nature Communications · doi:10.1038/s41467-024-48682-7 · Cited by 91
“The 3D printed and electroless Au-plated electrodes can be rapidly augmented for any anatomically optimized wearable and used/ re-used for long periods of time, WANDmini can support multi-day electrophysiological monitoring, and the presented of fl ine classi fi ers demonstrate the potential for…”
BackgroundSelf-citationKaveh, R., Doong, J., Zhou, A., Schwendeman, C., Gopalan, K., Burghardt, F., Arias, A. C., Maharbiz, M. M., & Muller, R. (2020) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2020.3001265 · Cited by 81
“Recording and digitization are performed by a custom neuromodulation IC [32] (NMIC, Cortera Neurotechnologies, Inc.”
MethodSelf-citationTrack-and-Zoom Neural Analog-to-Digital Converter With Blind Stimulation Artifact Rejection
Reza Pazhouhandeh, M., Chang, M., Valiante, T. A., & Genov, R. (2020) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2020.2991526 · Cited by 74
“3(a) shows a conventional ac-coupled recording AFE architecture.”
BackgroundInfluentialPu, H., Malekzadeh-Arasteh, O., Danesh, A. R., Nenadić, Z., Do, A. H., & Heydari, P. (2021) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2021.3108578 · Cited by 72
Zhao, W., Li, S., Xu, B., Yang, X., Tang, X., Shen, L., Lu, N., Pan, D. Z., & Sun, N. (2019) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2019.2959479 · Cited by 65
Pan, J., Abidi, A. A., Jiang, W., et al. (2019) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2018.2888884 · Cited by 57
A Fully Integrated Analog Front End for Biopotential Signal Sensing
Zheng, J., Ki, W., & Tsui, C.-Y. (2018) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2018.2854741 · Cited by 57
“Compared to [21]–[24], our design achieves better noise and linearity.”
Closed-loop electrical neurostimulation: Challenges and opportunities
Iturrate, I., Pereira, M., & Millán, J. D. R. (2018) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2018.09.007 · Cited by 54
“For example, indirect artifacts due to the capacitance of the electrodes and tissues can be reduced by ensuring that the positive and negative pulses have equal current amplitude [Johnson2017] or by careful selection of stimulation and recording parameters [Stanslaski2012].”
BackgroundRozgic, D., Hokhikyan, V., Jiang, W., Akita, I., Basir - Kazeruni, S., Chandrakumar, H., & Markovic, D. (2018) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2018.2889040 · Cited by 49
Lim, J., Moon, E., Barrow, M., Nason, S. R., Patel, P. R., Patil, P. G., Oh, S., Lee, I., Kim, H.-S., Sylvester, D., et al. (2020) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc19947.2020.9063005 · Cited by 49
“Brain machine interfaces using neural recording systems [1-4] can enable motor prediction [5-6] for accurate arm and hand control in paralyzed or severely injured individuals.”
BackgroundA Bidirectional Neural Interface SoC With Adaptive IIR Stimulation Artifact Cancelers
Samiei, A., & Hashemi, H. (2021) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2021.3056040 · Cited by 46
Jung, Y., Kweon, S.-J., Jeon, H., Choi, I., Koo, J., Kim, M. K., Lee, H. J., Ha, S., & Je, M. (2022) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2022.3188626 · Cited by 46
Nikas, A., Jambunathan, S., Klein, L., Voelker, M., & Ortmanns, M. (2019) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2019.2931811 · Cited by 45
“For an FS of 208 mVpp, the presented design achieves the highest linear dynamic range (90 dB), with [6] and [7] suffer-”
BackgroundA High-Voltage Compliance, 32-Channel Digitally Interfaced Neuromodulation System on Chip
Reich, S., Sporer, M., Haas, M., Becker, J., Schüettler, M., & Ortmanns, M. (2021) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2021.3076510 · Cited by 43
Lim, J., Lee, J., Moon, E., Barrow, M., Atzeni, G., Letner, J. G., Costello, J. T., Nason, S. R., Patel, P. R., Sun, Y., et al. (2022) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2022.3141688 · Cited by 38
An AC-Coupled 1st-Order Δ-ΔΣ Readout IC for Area-Efficient Neural Signal Acquisition
Yang, X., Ballini, M., Sawigun, C., Hsu, W.-Y., Weijers, J.-W., Putzeys, J., & López, C. M. (2023) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2023.3234612 · Cited by 38
“Direct-digitization topologies have been proposed as good alternatives to dramatically reduce the readout area and provide scalability in advanced technology nodes [2], [19], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43].”
MethodOpamp-Less Sub-μW/Channel Δ-Modulated Neural-ADC With Super-GΩ Input Impedance
Pazhouhandeh, M. R., Kassiri, H., Shoukry, A., Weisspapir, I., Carlen, P. L., & Genov, R. (2020) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2020.3041289 · Cited by 37
“3(b) shows neural-ADC (also known as ADC-direct or bio-ADC) architecture, which utilizes a chopped capacitively coupled instrumentation amplifier (CCIA) in the body of a high DR ADC to achieve high power and area efficiencies [8], [21].”
BackgroundCulaclii, S., Kim, B., Lo, Y.-K., Li, L., & Liu, W. (2018) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2018.2816464 · Cited by 34
The Layer 7 Cortical Interface: A Scalable and Minimally Invasive Brain–Computer Interface Platform
Hettick, M., Ho, E., Poole, A. J., Monge, M., Papageorgiou, D., Takahashi, K., LaMarca, M., Trietsch, D., Reed, K., Murphy, M., et al. (2022) · doi:10.1101/2022.01.02.474656 · Cited by 31
Pazhouhandeh, M. R., OrLeary, G., Weisspapir, I., Groppe, D. M., Nguyen, X.-T., Abdelhalim, K., Jafari, H. M., Valiante, T. A., Carlen, P. L., Verma, N., et al. (2019) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc.2019.8662458 · Cited by 32
“2 (middle) shows a state-of-the-art (SoA) architecture including an LNA and ADC combined in a single channel [3,4].”
Xu, J., Nguyen, A. T., Wu, T., Zhao, W., Luu, D. K., & Yang, Z. (2020) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2020.2972013 · Cited by 29
Adaptive Body Area Networks Using Kinematics and Biosignals
Moin, A., Thielens, A., Araújo, Á., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3003924 · Cited by 27
Self-citationKumari, L. S., & Kouzani, A. Z. (2022) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2022.3141369 · Cited by 27
Jiang, H., Ligouras, C., Nihtianov, S. N., et al. (2018) · IEEE Solid-State Circuits Letters · doi:10.1109/lssc.2018.2803447 · Cited by 25
“lower linearity [8] compared to the designs reported in [2] and [3].”
BackgroundMethodResultInfluentialZhou, Y., Bi, Z.-Y., Ji, M., Chen, S., Wang, W., Wang, K., Hu, B., Lü, X., & Wang, Z. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.2980294 · Cited by 24
A Neuromodulator Frontend With Reconfigurable Class-B Current and Voltage Controlled Stimulator
Haas, M., Vogelmann, P., & Ortmanns, M. (2018) · IEEE Solid-State Circuits Letters · doi:10.1109/lssc.2018.2827885 · Cited by 24
“High voltage (HV) CMOS technologies gained in popularity, as they allow the design of compact neuromodulators with high compliance stimulators and low noise recorders on one chip [2], [3].”
BackgroundChandrakumar, H., & Markovic, D. M. (2018) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc.2018.8310269 · Cited by 26
“and a CT ΔΣM in [2].”
BackgroundInfluentialReich, S., Sporer, M., & Ortmanns, M. (2021) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2021.3080398 · Cited by 23
“Area benefits due to technology scaling are not always exploitable in neuromodulation SoCs, as the implemented stimulator requires high-voltage (HV) compliant devices for efficient charge delivery into the tissue [10], [11], which are not readily available in smaller technology nodes.”
BackgroundJeong, K., Ha, S., & Je, M. (2023) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2023.3300928 · Cited by 25
“A number of implantable neural interface devices have been developed for the promising treatment of patients suffering from intractable diseases [1], [2], [3], [4], [5], [6].”
BackgroundKim, C., Joshi, S., Courellis, H. S., Wang, J., Miller, C. T., & Cauwenberghs, G. (2018) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc.2018.8310388 · Cited by 21
“Hybrid architectures utilizing oversampling ADCs with digital feedback [3-5] have seen recent adoption due to their increased power and area efficiency.”
BackgroundHuang, X., Ballini, M., Wang, S., Miccoli, B., Hoof, C. A. V., Gielen, G. E., Craninckx, J., Helleputte, N. V., & López, C. M. (2021) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2021.3111257 · Cited by 22
“The final version was published in IEEEXplore and IEEE owns the copyright. tolerance has also been implemented in [13]-[16].”
Hettick, M., Ho, E., Poole, A. J., Monge, M., Papageorgiou, D., Takahashi, K., LaMarca, M., Trietsch, D., Reed, K., Murphy, M. E., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01501-w · Cited by 43
An 800MΩ-Input-Impedance 95.3dB-DR Δ-ΔΣ AFE for Dry-Electrode Wearable EEG Recording
Li, Y., Li, Y., Li, H., Hong, Z., & Xu, J. (2024) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2024.3374891 · Cited by 21
“However, the maximum input signal range is typically less than 400mV pp [16], which may not be suf fi cient given the large DEO combined with multiple sources of interference.”
BackgroundMethodQiu, Z., Nguyen, A. T., Su, K., Yang, Z., & Xu, J. (2023) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2023.3321295 · Cited by 21
Energy-efficient bridge-to-digital converters
Jiang, H., & Makinwa, K. A. A. (2018) · Custom Integrated Circuits Conference · doi:10.1109/cicc.2018.8357027 · Cited by 18
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Self-citationKhan, S. R., Mugisha, A. J., Tsiamis, A., & Mitra, S. (2022) · Sensors · doi:10.3390/s22103635 · Cited by 16
“Furthermore, tethered implants are prone to bio-signal artifacts caused by patient movement [6].”
BackgroundHigh-density neural recording system design
Lee, H.-S., Eom, K., Park, M., Ku, S.-B., Lee, K., & Lee, H.-M. (2022) · Biomedical Engineering Letters · doi:10.1007/s13534-022-00233-z · Cited by 17
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“These techniques include: 1) back-end techniques, such as blanking and interpolation [2], matched filtering [3], component analysis [4] and 2) front-end techniques, such as reset [5], bandpass filtering [6], subtraction [7], [9], [10], and adaptive input ranging [11].”
MethodSelf-citationNeuroBus - Architecture for an Ultra-Flexible Neural Interface
Sporer, M., Vasilaş, I.-G., Adžemović, A., Graber, N., Reich, S., Gueli, C., Eickenscheidt, M., Diester, I., Stieglitz, T., & Ortmanns, M. (2024) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2024.3354785 · Cited by 13
A Time-Domain Analog Spatial Compressed Sensing Encoder for Multi-Channel Neural Recording
Okazawa, T., & Akita, I. (2018) · Sensors · doi:10.3390/s18010184 · Cited by 14
Resilient flow control for wireless data streaming in inductively coupled medical implants
Kempi, I., Ahmed, N., Hammer, A., Olabode, O., Unnikrishnan, V., Kosunen, M., & Ryynänen, J. (2019) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2019.102905 · Cited by 11
A Wireless, Multielectrode, User-generic Ear EEG Recording System
Kaveh, R., Doong, J., Zhou, A., et al. (2019) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2019.8918700 · Cited by 11
“The recording, digitization, and serialization are performed by a custom neuromodulation IC [12] (NMIC, Cortera Neurotechnologies, Inc.”
MethodSelf-citationActive Neural Interface Circuits and Systems for Selective Control of Peripheral Nerves: A Review
Habibollahi, M., Jiang, D., Lancashire, H. T., & Demosthenous, A. (2024) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2024.3430038 · Cited by 11
“Techniques such as adaptive filtering methods rely on the least-mean-squares (LMS) algorithm to implement infinite impulse response (IIR) and finite impulse response (FIR) digital filters to address and cancel out individual samples of artifacts [132], [194].”
Carlino, M. F., & Gielen, G. E. (2023) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2023.3344889 · Cited by 10
Mandal, A., Peña, D. M., Pamula, V. R., Khateeb, K., Murphy, L., Yazdan-Shahmorad, A., Perlmutter, S. I., Pape, F., Rudell, J. C., & Sathe, V. (2021) · Custom Integrated Circuits Conference · doi:10.1109/cicc51472.2021.9431493 · Cited by 10
“While existing systems [1-4] deliver electrical stimulation by placing electrodes in close proximity to target neurons, the capability for low-latency configurable stimulation that is spatially localized remains unrealized.”
BackgroundPu, H., Lim, J., Kellis, S., Liu, C. Y., Andersen, R. A., Do, A. H., Heydari, P., & Nenadić, Z. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab82ac · Cited by 10
“In contrast, existing artifact mitigation techniques, whether focused on analog front-ends [11, 12] or digital filtering [11, 13, 14], require that the recorded signals remain in the linear region (without saturation).”
BackgroundMethodWAND: A 128-channel, closed-loop, wireless artifact-free neuromodulation devicePreprint
Zhou, A., Santacruz, S. R., Johnson, B. C., Alexandrov, G., Moin, A., Burghardt, F., Rabaey, J. M., Carmena, J. M., & Muller, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.00556 · Cited by 10
“All power management is therefore integrated on the chip, enabling power from a single supply without the need for large off-chip power conversion circuits 20 .”
BackgroundMethodInfluentialSelf-citationLee, T., Park, J.-H., Chou, N., Cho, I., Kim, S., & Je, M. (2021) · IEEE Access · doi:10.1109/access.2021.3107299 · Cited by 9
Liu, H., Lin, Y., Qi, L., et al. (2022) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2022.3225559 · Cited by 8
“Compared with the high DR CTDSM [12], [17], [40], [41], high DR delta-modulator [6], [7], [9], adaptive filter assisted AFE [3] and hybrid fast feedback structure [37], the proposed front-end with the mixed domain level-crossing scheme achieves the largest artifact tolerance range and fastest recovery while maintainin…”
BackgroundMethodChen, C., Yang, J., Sands, J., Khan, M. A., & Poon, A. S. Y. (2022) · IEEE Solid-State Circuits Magazine · doi:10.1109/mssc.2022.3202738 · Cited by 7
“Therefore, a fast-settling mechanism has been included in several designs [27], [28], [29] to minimize the effects of such artifacts.”
BackgroundDynamic-Range-Enhancement Techniques for Artifact-Tolerant Biopotential-Acquisition ICs
Jung, Y., Kweon, S.-J., Lee, T., Jeong, K., Ha, S., & Je, M. (2022) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2022.3176272 · Cited by 8
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“Recent implementations address these problems by migrating to highvoltage-compliant CMOS processes for stimulation [2,3] where large core devices are less suited for digital integration, overdesigning the recording front-end dynamic range to accommodate large stimulus artifacts [3], or using mixed-signal feedback in t…”
BackgroundLee, T., & Je, M. (2020) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2020.3046451 · Cited by 7
“ing stimulation, a high voltage is usually applied to the CCS to accommodate a large voltage drop across ZETI, [61]–[63].”
BackgroundA 101-dB DR 2.2GΩ-Input-Impedance Direct Digitization ExG Front-End With Δ-Modulation
Li, Y., Li, H., Qu, T., et al. (2025) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2025.3563304 · Cited by 6
“A popular solution in recent years is the direct-digitization AFE (DD-AFE) [12], where a delta-sigma modulator ( ΔΣ M) is directly engaged to the electrodes, eliminating the need for an IA while providing a high dynamic range in a power-and area-efficient manner.”
Circuits and Architectures for Neural Recording Interfaces
López, C. M., & Huang, X. (2023) · Springer eBooks · doi:10.1007/978-3-031-28912-5_3 · Cited by 6
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A Sub-mm$^3$ Ultrasonic Free-floating Implant for Multi-mote Neural RecordingPreprint
Ghanbari, M. M., Piech, D. K., Shen, K., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.09386 · Cited by 5
“When the signal is downmodulated, the kT/C noise is converted into out-of-band chopper ripple [23].”
BackgroundSelf-citationAn Artifact-Resilient Neural Recording Front-end with Rail-to-Rail DM and CM Offset Correction
Bandali, M., & Johnson, B. C. (2023) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas46773.2023.10182181 · Cited by 4
“While this is sufficient for most neural recording applications [19], [20], chopping the input at a higher frequency than the sampling rate to reduce flicker noise reduces the input impedance to Zin = 1/(4fchopCAC) [9], [21].”
BackgroundSelf-citationArtifact-Aware Analogue/Mixed-Signal Front-Ends for Neural Recording Applications
Pérez-Prieto, N., Delgado‐Restituto, M., & Rodriguez-Vazquez, A. (2019) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2019.8702656 · Cited by 4
“Another potential problem is the long recovery time after blanking unless proper techniques are adopted [9], [10].”
BackgroundMEDUSA: A Low-Cost, 16-Channel Neuromodulation Platform with Arbitrary Waveform Generation
Tala, F., & Johnson, B. C. (2020) · Electronics · doi:10.3390/electronics9050812 · Cited by 4
Self-citationAdaptive EMG-based hand gesture recognition using hyperdimensional computing.Preprint
Moin, A., Zhou, A., Benatti, S., Rahimi, A., Alexandrov, G., Menon, A., Tamakloe, S., Ting, J., Yamamoto, N. A. D., Khan, Y., et al. (2019) · arXiv (Cornell University) · Cited by 4
Self-citationA Wide-Dynamic-Range, DC-Coupled, Time-Based Neural-Recording IC With Optimized CCO Frequency
Youn, D., Kim, Y.-I., Choi, I., Jung, Y., Jeon, H., Lee, K., Kweon, S.-J., Ha, S., & Je, M. (2024) · IEEE Access · doi:10.1109/access.2024.3424228 · Cited by 3
“2(b)) [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28].”
On the ESD Protection for 10V-Compliant Neural Stimulator in 65nm CMOS Technology
Ahmad, N., Lashkare, S., & Somappa, L. (2025) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas56072.2025.11044275 · Cited by 3
Nikas, A., Jambunathan, S., Klein, L., et al. (2018) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2018.8584687 · Cited by 3
“Linearization techniques for Gm-C integrator based ADCs include source degeneration, dynamic element matching [4] and capacitor coupled feedback architectures [7], [6].”
MethodSayed, G. M., Bartels, A., Dorigo, D. D., Fleiner, T., Roßkothen-Kuhl, N., & Kuhl, M. (2025) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2025.3563684 · Cited by 2
“In such scenarios, it is advantageous to increase the dynamic range of the recording frontend, to accommodate larger artifacts without saturation [9], [10].”
A Wearable Platform for Real-Time Control of a Prosthetic Hand by High-Density EMG
Molinari, R. G., Avilés-Carrillo, V., Villa, G. A. G. D., & Elias, L. A. (2026) · IEEE Access · doi:10.1109/access.2026.3670500 · Cited by 3
Technical Review: Neural Recording Circuits for Bidirectional Neural Interface
Jung, Y., Jeon, H., Lee, T., Seong, H. Y., Park, C., & Je, M. (2018) · IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia) · doi:10.1109/icce-asia.2018.8552141 · Cited by 2
“The design in [9] employs a similar topology based on the 1-”
Design of CMOS Circuits for Electrophysiology
Helleputte, N. V., López, C. M., & Hoof, C. A. V. (2023) · IEICE Transactions on Electronics · doi:10.1587/transele.2022cti0003 · Cited by 2
“…kT / C noise is usually not a big concern since it gets averaged and becomes negligible compared to the overall ADC input-referred noise due to the very infrequent reset operation [38], or converted into out-of-band chopper ripple and then filtered out by the subsequent integrator [45].”
Brain-inspired Multi-level Control of an Assistive Prosthetic Hand through EMG Task Recognition
Menon, A., Olascoaga, L. I. G., Shakouri, N., Ruffing, J., Balanaga, V., & Rabaey, J. M. (2022) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas54905.2022.9948571 · Cited by 2
“The adapter PCB was re-designed to interface 5 force sensors and the flexible 64-channel sEMG electrode array with the WAND device [26] - an 8-layer PCB that accommodates an accelerometer, the custom-designed sensing and digitizing ASIC [27], and components for processing/telemetry.”
MethodSelf-citationA Neural Recording and Stimulation Chip with Artifact Suppression for Biomedical Devices
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“Reference [49] (REC and STIM) Reference [50] (REC and STIM) Reference [37] (REC and STIM) Reference [40] (REC and STIM) Reference [24] (REC and STIM) +is work (REC and STIM) Process 0.”
BackgroundFeasibility of Using the Utah Array for Long-Term Fully Implantable Neuroprosthesis Systems
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Design Considerations for Implantable Neural Circuits and Systems
Hsu, W.-Y., Hsieh, P.-H., & Chen, H. (2023) · Handbook of Neuroengineering · doi:10.1007/978-981-16-5540-1_19 · Cited by 1
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“Both DBS and FSCV generate stimulation artifacts as high as 100 mV at the interface of the neural recording analog front end (AFE), which can cause it to saturate [7], [8], [9], [10].”
A wireless, 60-channel, AI-enabled neurostimulation platform
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“Moreover, only [9] considers stimulator charge balance even though the link between residual charge in the electrodes (i.”
BackgroundAn Offset Charge Compensating Biphasic Neuro - stimulation for Faradaic DC-Current Reduction
Cho, D., Koo, N., Jang, T., et al. (2021) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas51556.2021.9401722 · Cited by 1
Biomimetic bidirectional hand neuroprostheses for restoring somatosensory and motor functions
Iberite, F., Mendez, V., Mazzoni, A., Shokur, S., & Micera, S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822828-9.00011-3 · Cited by 2
Restoring upper extremity function with brain-machine interfaces
Nason, S. R., Mender, M. J., Letner, J. G., Chestek, C. A., & Patil, P. G. (2021) · International review of neurobiology · doi:10.1016/bs.irn.2021.06.001 · Cited by 2
An Implantable 8-Contact Sense and Stimulation System for Continuous Recording
Gahnz, J., Doerr, J., & Orser, H. D. (2021) · IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs53924.2021.9665529 · Cited by 1
“Prior work reported in the literature describing neuromodulation sense and stimulation module designs [6][10] have focused on different system considerations including designing for electrodes with significantly different impedances and designing for use in animal models where safety and reliability concerns are reduc…”
BackgroundAdvances in High-Resolution, Miniaturized Bioelectrical Neural Interface Design
Nguyen, A. T., Xu, J., Wu, T., Luu, D. K., & Yang, Z. (2023) · Handbook of Neuroengineering · doi:10.1007/978-981-16-5540-1_23 · Cited by 1
Bandali, M., Riley, M., & Johnson, B. C. (2024) · Conference proceedings · doi:10.1109/mwscas60917.2024.10658812 · Cited by 1
“The chopping technique mitigates flicker and kT/C noise using double integration with opposite polarities [12], [13], maintaining a small size for the input devices in the Gm-cell (NMOS: W/L = 8 × 2 .”
MethodSelf-citationAriani, A., Koesoema, A. P., & Soegijoko, S. (2024) · Disaster risk reduction · doi:10.1007/978-981-97-2049-1_3 · Cited by 1
Zhu, B., Shin, U., & Shoaran, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.05848
“Recent improvements allow the amplifier to be connected immediately after stimulation [56], using mixed-signal circuit realization of the analog front-end (AFE).”
BackgroundEnergy-Efficient Electrical Stimulation Systems
Banerjee, A., & Nag, S. (2021) · Handbook of Neuroengineering · doi:10.1007/978-981-15-2848-4_104-2
Design Considerations for Implantable Neural Circuits and Systems
Hsu, W.-Y., Hsieh, P.-H., & Chen, H. (2021) · Handbook of Neuroengineering · doi:10.1007/978-981-15-2848-4_19-1
Advances in High-Resolution, Miniaturized Bioelectrical Neural Interface Design
Nguyen, A. T., Xu, J., Wu, T., Luu, D. K., & Yang, Z. (2021) · Handbook of Neuroengineering · doi:10.1007/978-981-15-2848-4_23-2 · Cited by 1
Metadata
- Authors
- Johnson, B. C., Gambini, S., Izyumin, I., Moin, A., Zhou, A., Alexandrov, G., Santacruz, S. R., Rabaey, J. M., Carmena, J. M., & Muller, R.
- Affiliations
- University of California, Berkeley (US)
- ORCID
- Venue
- 2017 Symposium on VLSI Circuits
- Publisher
- IEEE
- Published
- 2017-06
- Pages
- C48-C49
- ISSN
- 2166-9600 (ISSN-L)
- OpenAlex
- W2742939082
- Cited by
- 119
- References
- 5
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 10 · 2023: 10 · 2022: 12 · 2021: 16 · 2020: 15 · 2019: 16 · 2018: 18 · 2017: 1
- FWCI
- 27.15
- Type
- conference paper
- Language
- en
- Open access
- green
- escholarship.org
- Locations
- Symposium on VLSI Circuits (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Analog and Mixed-Signal Circuit Design
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Radio Frequency Integrated Circuit Design
- Keywords
- neurostimulation, artifact recovery, low-power CMOS, HV-CMOS, dynamic power management
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