用于无线、微型和大规模神经接口的频率切换感应式功率传输系统。

Gian Luca Barbruni;Claudia Cordara;Marco Carminati;Sandro Carrara;Diego Ghezzi
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引用次数: 0

摘要

三线圈感应式功率传输是为多个微型神经植入体供电的最先进解决方案。然而,最大传输功率受限于供电链路的效率和安全限制。在这里,我们提出了一种频率切换感应链路,即用主动谐振器取代通常在三线圈链路中使用的被动谐振器。它通过低频 13.56 MHz 的双线圈感应链路从外部发射器接收电源。然后,它通过专用电路将工作频率切换到较高的 433.92 MHz 频率。我们的模拟报告显示,在安全限制条件下,功率传输效率为 0.013%,向负载传输的最大功率为 1970 μ W,与同等的无源三线圈链路相比,分别高出两个数量级和六十多个数量级。频率开关感应系统是一种可扩展的多功能解决方案,适用于无线、小型化和大规模神经接口。
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A Frequency-Switching Inductive Power Transfer System for Wireless, Miniaturised and Large-Scale Neural Interfaces
Three-coil inductive power transfer is the state-of-the-art solution to power multiple miniaturised neural implants. However, the maximum delivered power is limited by the efficiency of the powering link and safety constrains. Here we propose a frequency-switching inductive link, where the passive resonator normally used in a three-coil link is replaced by an active resonator. It receives power from the external transmitter via a two-coil inductive link at the low frequency of 13.56 MHz. Then, it switches the operating frequency to the higher frequency of 433.92 MHz through a dedicated circuitry. Last, it transmits power to 1024 miniaturised implants via a three-coil inductive link using an array of 37 focusing resonators for a brain coverage of 163.84 mm $^{2}$ . Our simulations reported a power transfer efficiency of 0.013 $\%$ and a maximum power delivered to the load of 1970 $\mu$ W under safety-constrains, which are respectively two orders of magnitude and more than six decades higher compared to an equivalent passive three-coil link. The frequency-switching inductive system is a scalable and highly versatile solution for wireless, miniaturised and large-scale neural interfaces.
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Table of Contents Erratum to “Design of an Extreme Low Cutoff Frequency Highpass Frontend for CMOS ISFET via Direct Tunneling Principle” IEEE Transactions on Biomedical Circuits and Systems Publication Information IEEE Circuits and Systems Society Information Guest Editorial: Ultralow-Power Technologies for Edge Computing in Human-Machine Interface Applications
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