High‐Density Flexible Neural Implants with Submicron Feedline Resolution
Abstract The development of high‐density microelectrode arrays (MEAs) for large‐scale brain recordings requires neural probes with reduced footprints to minimize tissue damage. One way to achieve this is by implementing dense electrode arrays with narrower feedline dimensions, though this increases susceptibility to capacitive coupling between electrical interconnects. To address this, this study explores the resolution limits for high‐density flexible MEAs by optimizing the fabrication using optical contact lithography (OCL) and electron beam lithography (EBL). OCL enables metal feedlines with widths of 520 nm and interconnect spaces of 280 nm, while EBL allows the realization of 50 nm feedlines with 150 nm spaces on flexible parylene C substrates. Based on these techniques, we fabricate a flexible 64‐channel intracortical implant with a miniaturized cross‐section of only 50 × 6 or 70 × 6 µm 2 . In vivo validation in awake rats demonstrates that the fabricated, high‐density flexible intracortical implants with submicron feedline resolution offer low‐impedance electrodes and reduced crosstalk, enabling reliable neuronal recordings. These findings demonstrate the feasibility of miniaturizing flexible MEAs using a single‐metal layer process, thereby reducing manufacturing complexity in high‐density thin‐film polymer‐based neural interfaces.
Authors
- Thomas Grap
- George G. Malliaras (ORCID: https://orcid.org/0000-0002-4582-8501)
- Viviana Rincón Montes (ORCID: https://orcid.org/0000-0001-8123-6805)
- Alejandro Carnicer‐Lombarte (ORCID: https://orcid.org/0000-0002-5650-4692)
- Martin Kasavetov
- Lina Koschinski (ORCID: https://orcid.org/0009-0002-8425-6291)
- Simon Decke
- Marie Jung
- Erkan Yilmaz
- Marius Kleutgens
Institutions
- Forschungszentrum Jülich (DE)
- University of Cambridge (GB)
- Cambridge Microelectronics (United Kingdom) (GB)
- RWTH Aachen University (DE)
- FH Aachen (DE)
Publication Details
- Journal
- Advanced Electronic Materials
- Published
- 2025-04-22
- DOI
- https://doi.org/10.1002/aelm.202500088
- Citations
- 4
- Primary Topic
- Neuroscience and Neural Engineering
- Type
- article
- Field-Weighted Citation Impact
- 3.64
Funders
- University of Cambridge
- Deutsche Forschungsgemeinschaft
- Forschungszentrum Jülich