A 32-Channel 85.4dB SNDR Time-multiplexed Neural Recording Frontend Achieving Within-Conversion Artifact Recovery
Next-generation large-scale implantable neuromodulation systems require a highdensity and ultra-low power recording front-end (RFE). Time-division multiplexing (TDM), sharing multiple channels with a single neural RFE, has proven effective in reducing per-channel area and power [1]–[5]. Their recent use in active microelectrode arrays has reduced aggregate wire count and packaging complexity [3]. TDM-based RFEs utilizing continuous-time incremental delta-sigma modulation (CT-IDSM) [3]–[5] for direct digitization effectively optimize the signal path, reducing silicon area further while delivering high-resolution data. These RFEs capture low amplitude neural signals (up to a few mVs) from channels, employing a separate servo loop to withstand electrode DC offsets (EDOs) up to tens of mVs. However, high-frequency stimulation artifacts (> 1kHz) and low-frequency motion artifacts (< 10Hz) limit the resolution of Nyquist samples in these systems. This degradation in signal quality is especially pronounced in large-scale closed-loop neuromodulation systems which experience frequent stimulation events.
Authors
- Wei-En Lee (ORCID: https://orcid.org/0000-0001-6554-3362)
- Amanda Jacob
- Visvesh Sathe (ORCID: https://orcid.org/0000-0001-7950-4495)
- Julian Arenas
- Shaolan Li (ORCID: https://orcid.org/0000-0002-2736-5451)
- Muhannad S. Bakir (ORCID: https://orcid.org/0000-0002-0380-0842)
- Chi-Hsiang Huang (ORCID: https://orcid.org/0000-0003-4147-4643)
- Philip Anschutz (ORCID: https://orcid.org/0009-0003-9287-5671)
- Samuel J. Sober (ORCID: https://orcid.org/0000-0002-1140-7469)
- Arindam Mandal (ORCID: https://orcid.org/0000-0001-5944-2733)
- Keshav Ramachandra
Institutions
- Georgia Institute of Technology (US)
- Emory University (US)
Publication Details
- Published
- 2025-04-13
- DOI
- https://doi.org/10.1109/cicc63670.2025.10983716
- Citations
- 1
- Primary Topic
- Integrated Circuits and Semiconductor Failure Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.73
Funders
- National Science Foundation