A 3 mm<sup>2</sup>, Energy-Efficient, Multi-Data-Rate, FDMA Transmitter with On-Chip Antenna for Next-Generation Neural Implants
This paper presents a versatile energy-efficient wireless transmitter (TX) for neural implants with high/ultrahigh channel counts. The TX supports a wide range of data transmission rates, from $50 \mathrm{Mb} / \mathrm{s}$ to $1.2 \mathrm{~Gb} / \mathrm{s}$, through a novel dual-modulation approach. Our design combines 4-pulse position modulation (4PPM) and 2-phase shift keying (2PSK) for high data throughput, while adopting simple on-off keying (OOK) for energy efficiency at lower data rates. To support this modulation strategy, a twin oscillator-based wideband phase-locked loop (PLL) is proposed to generate the RF carrier at ultra-wideband (UWB). Thanks to the relaxed phase noise requirement for OOK modulation, a $5 \times$ frequency multiplier with edge combination is utilized, enabling a duty-cycled PLL-based RF carrier, thus enhancing energy efficiency. The proposed TX was fabricated in $65-\mathrm{nm}$ CMOS and consumes $1.49-7.26 \mathrm{~mW}$ across all supported data rates $(50-1200 \mathrm{Mb} / \mathrm{s})$. Our TX further incorporates frequency division multiple access (FDMA) for simultaneous, untethered multi-node operation in distributed brain implants. In addition, a custom-designed on-chip antenna facilitates a compact footprint of only $3 \mathrm{~mm}^{2}$, while achieving a record transmission range of 0.6 m compared to state-of-the-art implantable TXs. Index Terms-Impulse-radio ultra-wideband transmitter (IRUWB TX), on-chip antenna, multi-data-rate, frequency division multiple access (FDMA), duty-cycling, neural implants.
Authors
- Mahsa Shoaran (ORCID: https://orcid.org/0000-0002-6426-4799)
- Mingxiang Gao (ORCID: https://orcid.org/0000-0003-3798-3533)
- Anja K. Skrivervik (ORCID: https://orcid.org/0000-0002-5931-6400)
- Cong Ding (ORCID: https://orcid.org/0009-0000-3267-9941)
Institutions
- École Polytechnique Fédérale de Lausanne (CH)
Publication Details
- Published
- 2024-09-09
- DOI
- https://doi.org/10.1109/esserc62670.2024.10719570
- Primary Topic
- Neuroscience and Neural Engineering
- Type
- article
- Field-Weighted Citation Impact
- 0.00