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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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rhew (2014) · IEEE JSSC
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lo (2016) · IEEE ISSCC
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muller (2015) · IEEE JSSC
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sun (2014) · Neurotherapeutics
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Items 101–119 of 119
An Onchip Electrode Impedance Estimation achieving 1.2 dB 3σ-Accuracy with Minimum Hardware Overhead
Reich, S., Ammann, P., Sporer, M., & Ortmanns, M. (2022) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas54905.2022.9948672
Energy-Efficient Electrical Stimulation Systems
Banerjee, A., & Nag, S. (2023) · Handbook of Neuroengineering · doi:10.1007/978-981-16-5540-1_104
On-Chip Active Pulse-Clamp Stimulation (APCS) for Rapid Recovery, Charge-Balanced Neural Stimulation
Tala, F., & Johnson, B. C. (2024) · IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) · doi:10.1109/mwscas60917.2024.10658858
“Closed-loop neuromodulation is a promising advancement that delivers dynamic treatment based on the physiological response from the stimulation site [1].”
BackgroundSelf-citationA 10.7‐μA 103‐dB DR Hybrid Delta‐Sigma Modulator for Sensing Applications
Zhang, H., Wu, X., Tang, Z., et al. (2024) · International Journal of Circuit Theory and Applications · doi:10.1002/cta.4303
“Open-loop integrators, like the combination of a transconductance stage with a current-controlled oscillator [25–27] or Gm-C integrators [13], typically exhibit greater energy efficiency compared to closed-loop integrators.”
Li, C., Wang, C., Zhu, Y., et al. (2024) · Communications in computer and information science · doi:10.1007/978-981-96-0188-2_6
Orser, H. D., & Doan, P. (2025) · IEEE Access · doi:10.1109/access.2025.3569414
“Several prior studies have investigated sense and stimulation circuits for neuromodulation applications [9], [10], [11], [12], [13].”
Mandal, A., Peña, D. M., Pamula, V. R., Perlmutter, S. I., Pape, F., Rudell, J. C., & Sathe, V. (2025) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2025.3578284
“Table I provides a summary of specifications along with a comparison with state-of-the-art stimulator circuits [12], [13], [14], [17], [29].”
He, J., Xu, J., & Wang, Y. (2025) · Micromachines · doi:10.3390/mi16101176
Artifact Cancellation in Neurostimulators: A Review
Das, V. R., Karşilayan, A. İ., Silva-Martínez, J., Genzer, D., & Stotts, L. J. (2025) · IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) · doi:10.1109/mwscas53549.2025.11244388
Huang, X., Yassin, H. A., Alcazar, B. L., Akhoundi, A., & Muratore, D. G. (2026) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2025.3650066
“3 (right), existing ECoG AFEs prioritize low-noise recordings [14], [16], [18], [19], [26], [33], [34], achieving noise levels of ∼ 1-3 µ V rms at the expense of high power; designs targeting high dynamic range (DR) su ff er from an even less favorable power-noise tradeo ff [35], [36], [37].”
Lakatos, A., Riley, M., Mokogwu, F. N., et al. (2025) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas67066.2025.00093
Self-citationLiu, Y., Yuan, S., Wu, Y., et al. (2025) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas67066.2025.00089
“Moreover, the high-gain analog front-end necessitates low-noise circuitry, which leads to increased power consumption [11].”
ESD Protection With Sub-3Ω Dynamic Resistance for Neuromodulation System-on-Chip
Das, T., Ahmad, N., Somappa, L., & Lashkare, S. (2026) · IEEE Journal of the Electron Devices Society · doi:10.1109/jeds.2026.3663915
“The H-bridge topology is incorporated into the neurostimulators to ensure charge balance between the two phases of biphasic stimulation, using a single current source [1].”
Design of ESD Protection with on-chip diodes for 65nm CMOS High Voltage Neurostimulators
Das, T., Somappa, L., & Lashkare, S. (2025) · IEEE International Conference on Emerging Electronics (ICEE) · doi:10.1109/icee67165.2025.11409724
“This is especially true for implantable medical devices, such as neurostimulator [1], [2], where device failure can compromise both patient safety and therapeutic effectiveness [3]–[5].”
Fabrication of High‐Density Multimodal Neural Probes Based on Heterogeneously Integrated CMOS
Mun, J. H., Kim, M., Shin, W., et al. (2026) · Advanced Science · doi:10.1002/advs.202524260
Jeong, K., Livanelioglu, C., Liao, J., Lee, I., & Jang, T. (2026) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2026.3685168 · Cited by 1
“DigitalObjectIdentifier 10.1109 / JSSC.2026.3685168 recent advancement of implantable and wearable biomedical devices, such as implantable neural interfaces for neurological disorders [1], [2], [3], [4] and wearable devices for various health monitoring applications.”
InfluentialApplying Cyber Informed Engineering Principles to Secure SoC Design for Electric Vehicle Charging
Peyal, M. M. K., Lampe, B., Loo, S. M., et al. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-33701-6_4
Self-citationBidirectional Neural Interface for Real-time In-Vivo ECoG Co-Acquisition in Acute Animal Models
Sheeraz, M., Jiang, J., Zhang, J., et al. (2026) · Conference proceedings · doi:10.1109/mwscas67364.2026.11680614
“Electrocorticogram (ECoG) captures local field potentials (LFPs) from brain’s cortical surface, striking a practical balance between spatial precision of penetrating implanted electrode arrays and noninvasive nature of scalp ECoG [1], [2].”
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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