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.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:89920 |
| Date | 21 February 2024 |
| Creators | Schüffny, 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 |
| Contributors | Tu Dresden |
| Publisher | IEEE - Institut of Electrical and Electronics Engineers |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/acceptedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 978-1-6654-7143-5, 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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