A 340-mg Fully Implantable Bidirectional Neuromodulation System Incorporating a Customized ASIC

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Authors: Wenkai Jin, Liyang Wang, Yu Xia, Haochuan Wang, Guo Qing Luo, Hui Hong, Yuhua Cheng, Mang I Vai, Sio Hang Pun, Nenggan Zheng
Cited by: 0
References: 45

Abstract

Fully implantable brain-computer interfaces (BCIs) effectively mitigate the risk of infection while achieving high signal-to-noise ratio (SNR) neural signal recording, providing a reliable and efficient approach for decoding complex neural signals. However, challenges remain in miniaturization, efficient wireless power delivery, and biosafety monitoring. A fully implantable, wireless, battery-free BCI microsystem is realized as a hybrid PCB assembly (0.33 cm³, 340 mg), with weight and volume reduction achieved through high-Q coil (Q-factor = 157) integration for battery-free operation and hybrid module integration. The ASIC customized chip utilizes capacitive coupling and a pseudo-resistor-based feedback loop for effective DC offset removal and low-frequency signal recording, while also supporting current-mode output for electro-optical multimodal stimulation. Additionally, a 16-bit high-precision temperature sensor enables real-time thermal monitoring, ensuring robust thermal management and mitigating risks of tissue damage. Compared to lightest reported implantable BCI systems, a 66% reduction in weight and an 84% reduction in size are achieved by this design. Benchtop validation and in-situ saline tests confirm the capability of the system for wireless neural signal recording and current stimulation functionality. With a power consumption of 29.7 mW, the temperature rise during 6 hours of continuous operation in an agar-encapsulated simulated tissue environment remains below 0.175°C, effectively preventing thermal damage to surrounding tissue. This microsystem offers a promising platform for investigating neural modulation mechanisms and brain science research in small animals, such as rats and rabbits.

Fields of Study & Subjects

Neuroscience and Neural Engineering Analog and Mixed-Signal Circuit Design Wireless Body Area Networks

Publication details

Journal / source
IEEE Sensors Journal
Volume
26
Issue
4
Pages
5697–5709
Publication date
12 January 2026