Extreme manufacturing of minimally invasive intracortical neural electrodes
Abstract Small and flexible penetrating neural electrodes have recently emerged as a promising technology for both fundamental brain science and advanced brain-computer interfaces. These neural electrodes are designed with exceptional flexibility and adaptability to seamlessly interact with delicate neural tissue, enabling sub-millisecond recording of individual neurons and stimulation of small neuronal clusters in the brain. In this review, we analyze the fundamental physical constraints imposed on electrical neural interfacing and highlight the significant progress achieved by those minimally invasive neural probes over the past decade. Furthermore, we discuss the research needs in manufacturing techniques, materials science, as well as surface functionalization processes for improving the long-term stability and reliability of neural electrodes. Finally, we summarize the emerging trends and outline the technological challenges faced in this rapidly evolving field.
Authors
- Guangdong Zhou (ORCID: https://orcid.org/0000-0003-2488-2733)
- Fei He (ORCID: https://orcid.org/0000-0002-6009-8957)
- Xiaoling Wei (ORCID: https://orcid.org/0000-0002-0099-2260)
- Ruxin Li
- Mingliang Xu (ORCID: https://orcid.org/0000-0002-6885-3451)
- Artem Iliasov (ORCID: https://orcid.org/0000-0002-2444-4306)
- Chengyu Li (ORCID: https://orcid.org/0000-0002-4434-0838)
- Zhen Chen (ORCID: https://orcid.org/0000-0003-1723-0251)
- Jingsong Wu
- Ziyi Zhu (ORCID: https://orcid.org/0009-0008-4417-2644)
- Haoran Ma (ORCID: https://orcid.org/0000-0001-5598-739X)
- W.M. Tsang
Institutions
- Songjiang District Central Hospital (CN)
- Shanghai Institute of Optics and Fine Mechanics (CN)
- Shanghai Institute of Microsystem and Information Technology (CN)
- Huashan Hospital (CN)
Publication Details
- Journal
- International Journal of Extreme Manufacturing
- Published
- 2025-11-27
- DOI
- https://doi.org/10.1088/2631-7990/ae2579
- Citations
- 2
- Primary Topic
- Neuroscience and Neural Engineering
- Type
- article
- Field-Weighted Citation Impact
- 1.82