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A 64-channel back-gate adapted ultra-low-voltage spike-aware neural recording front-end with on-chip lossless/near-lossless compression engine and 3.3V stimulator in 22nm FDSOI

In neural implants and biohybrid research systems, the integration of electrode recording and stimulation front-ends with pre-processing circuitry promises a drastic increase in real-time capabilities [1,6]. In our proposed neural recording system, constant sampling with a bandwidth of 9.8kHz yields 6.73μV input-referred noise (IRN) at a power-per-channel of 0.34μW for the time-continuous ΔΣ−modulator, and 0.52μW for the digital filters and spike detectors. We introduce dynamic current/bandwidth selection at the ΔΣ and digital filter to reduce recording bandwidth at the absence of spikes (i.e. local field potentials). This is controlled by a two-level spike detection and adjusted by adaptive threshold estimation (ATE). Dynamic bandwidth selection reduces power by 53.7%, increasing the available channel count at a low heat dissipation. Adaptive back-gate voltage tuning (ABGVT) compensates for PVT variation in subthreshold circuits. This allows 1.8V input/output (IO) devices to operate at 0.4V supply voltage robustly. The proposed 64-channel neural recording system moreover includes a 16-channel adaptive compression engine (ACE) and an 8-channel on-chip current stimulator at 3.3V. The stimulator supports field-shaping approaches, promising increased selectivity in future research.

Identiferoai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:89920
Date21 February 2024
CreatorsSchüffny, Franz Marcus, Zeinolabedin, Seyed Mohammad Ali, George, Richard, Guo, Liyuan, Weiße, Annika, Uhlig, Johannes, Meyer, Julian, Dixius, Andreas, Hänzsche, Stefan, Berthel, Marc, Scholze, Stefan, Höppner, Sebastian, Mayr, Christian
ContributorsTu Dresden
PublisherIEEE - Institut of Electrical and Electronics Engineers
Source SetsHochschulschriftenserver (HSSS) der SLUB Dresden
LanguageEnglish
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/acceptedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text
Rightsinfo:eu-repo/semantics/openAccess
Relation978-1-6654-7143-5, 10.1109/A-SSCC56115.2022.9980793, info:eu-repo/grantAgreement/European Commission/H2020 | RIA/824162//A SYnaptically connected brain-silicon Neural Closed-loop Hybrid system/SYNCH, info:eu-repo/grantAgreement/Bundesministerium für Bildung und Forschung/6G-life/16KISK001K//Souverän. Digital. Vernetzt.

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