15.4 A Neuroprosthetic SoC with Sensory Feedback Featuring Frequency-Splitting-Based Wireless Power Transfer with 200Mb/s 0.67pJ/b Backscatter Data Uplink and Unsupervised Multi-Class Spike Sorting

作者：Yu Huang, B. Liu, Yuhan Hou, Jianxiong Xu, Hao You, Ashley Hung, Swarnava Ghosh, Eric Zhi Feng Liu, Naize Yang, Jun‐Yu Ma, Hanfeng Cai, Laura Kondrataviciute, Qin‐Pei Deng, Suneil K. Kalia, Andrew G. Richardson, Ping-Hsuan Hsieh, Roman Genov, Xilin Liu · 年份：2025 · DOI：10.1109/isscc49661.2025.10904677 · 被引用次数：7 · 研究领域：Energy Harvesting in Wireless Networks、Wireless Power Transfer Systems、Conducting polymers and applications

Neuroprosthetic technology has made significant strides in restoring movement for paralyzed individuals by decoding motor cortex signals into commands for prosthetic limbs or exoskeletons. Although high-channel neural implants have enhanced prosthetic control and freedom of movement, the benefits of channel scaling are restricted by the absence of feedback and the steep learning curves required for users. To address this, sensory feedback from prosthetic sensors has been used to modulate the sensory cortex for more stable and accurate prosthetic control [1]. However, latency in data transmission, decoding, and feedback mapping hinders the system's effectiveness as a true closed-loop. To overcome these challenges, a disruptive approach has emerged that introduces rapid local feedback between the motor and sensory cortices [2]. Instead of relying on external sensor inputs, this method derives sensory feedback directly from motor signals and modulates stimulation in the sensory cortex, reducing latency and simplifying learning (Fig. 15.4.1 top). Animal studies using optogenetic feedback have shown faster motor target acquisition with this internal feedback [3]. While optogenetics cannot be easily adopted for humans, electrical stimulation is a viable alternative, though it presents challenges such as rejecting stimulation artifacts to avoid false modulation or positive feedback loops.