Next generation implantable neural interfaces are targeting devices with mm-scale form factors that are freely floating and completely wireless. Scalability to more recording (or stimulation) channels will be achieved through distributing multiple devices, instead of the current approach that uses a single centralized implant wired to individual electrodes or arrays. In this way, challenges associated with tethers, micromotion, and reliability of wiring is mitigated. This concept is now being applied to both central and peripheral nervous system interfaces. One key requirement, however, is to maximize specific absorption rate (SAR) constrained achievable wireless power transfer efficiency (PTE) of these inductive links with mm-sized receivers. Chip-scale coil structures for microsystem integration that can provide efficient near-field coupling are investigated. We develop near-optimal geometries for three specific coil structures: in-CMOS, above-CMOS (planar coil post-fabricated on a substrate), and around-CMOS (helical wirewound coil around substrate). We develop analytical and simulation models that have been validated in air and biological tissues by fabrications and experimental measurements. Specifically, we prototype structures that are constrained to a 4 mm 4 mm silicon substrate, i.e., the planar in-/above-CMOS coils have outer diameters 4 mm, whereas the around-CMOS coil has an inner diameter of 4 mm. The in-CMOS and above-CMOS coils have metal film thicknesses of 3- m aluminium and 25- m gold, respectively, whereas the around-CMOS coil is fabricated by winding a 25-m gold bonding wire around the substrate. The measured quality factors (Q) of the mm-scale Rx coils are 10.5 @450.3 MHz (in-CMOS), 24.61 @85 MHz (above-CMOS), and 26.23 @283 MHz (around-CMOS). Also, PTE of 2-coil links based on three types of chip-scale coils is measured in air and tissue environment to demonstrate tissue loss for bio-implants. The SAR-constrained maximum PTE measured (together with resonant frequencies, in tissue) are 1.64% @355.8 MHz (in-CMOS), 2.09% @82.9 MHz (above-CMOS), and 3.05% @318.8 MHz (around-CMOS).

We present a platform technology encompassing a family of innovations that together aim to tackle key challenges with existing implantable brain machine interfaces. The ENGINI (Empowering Next Generation Implantable Neural Interfaces) platform utilizes a 3-tier network (external processor, cranial transponder, intracortical probes) to inductively couple power to, and communicate data from, a distributed array of freely-floating mm-scale probes. Novel features integrated into each probe include: (1) an array of niobium microwires for observing local field potentials (LFPs) along the cortical column; (2) ultra-low power instrumentation for signal acquisition and data reduction; (3) an autonomous, self-calibrating wireless transceiver for receiving power and transmitting data; and (4) a hermetically-sealed micropackage suitable for chronic use. We are additionally engineering a surgical tool, to facilitate manual and robot-assisted insertion, within a streamlined neurosurgical workflow. Ongoing work is focused on system integration and preclinical testing. I. INTRODUCTION Brain Machine Interfaces (BMIs) have a genuine opportunity to effect a transformative impact on both medical [1], [2] and non-medical [3] applications. More specifically, clinical translation can lead to the restoration of movement and communication in patient populations with tetraplegia, amylotrophic lateral sclerosis, locked-in-syndrome, and speech disturbances. Current translational efforts utilize implantable medical devices (IMDs), e.g. Medtronic PC+S [1], experimental neuroscience tools, e.g. Blackrock Neuroport [2], or engineer new devices leveraged on IMDs [4], [5]. A. Key Challenges The major technical challenges with state-of-the-art BMI technology are chronic reliability (device longevity, recording stability, calibration/training) and scalability (extending number of recording and/or stimulation sites). In tackling these, wireless capability is crucial, but brings on its own set of challenges (wireless transfer efficiency, data throughput).

Abstract-This paper proposes a novel method for integrating CMOS microelectronics with microwire-based electrodes for next generation implantable brain machine interfaces. There is strong evidence to suggest that microwire-based electrodes outperform micromachined and polymer-based electrodes in terms of signal integrity and chronic viability. Furthermore, it has been shown that the recording of Local Field Potentials (LFPs) is more robust to tissue damage and scar tissue growth when compared to action potentials. This work therefore investigates the suitability of microwire electrodes for LFP recording by studying the electrical properties of key materials. We identify Niobium (Nb) as a candidate material with highly desirable properties. There is however also an inherent incompatibility when it comes to connection of microwire-based electrodes to silicon chips. Here we present a new process flow utilising a recessed glass substrate for mechanical support, silicon interposer for interconnection, and electroplating for contact adhesion. Furthermore, the proposed structure lends itself to hermetic encapsulation towards gas cavity based micropackages.

Abstract-Next generation brain machine interfaces fundamentally need to improve the information transfer rate and chronic consistency when observing neural activity over a long period of time. Towards this aim, this paper presents a novel System-on-Chip (SoC) for a mm-scale wireless neural recording node that can be implanted in a distributed fashion. The proposed self-regulating architecture allows each implant to operate autonomously and adaptively load the electromagnetic field to extract a precise amount of power for full-system operation. This can allow for a large number of recording sites across multiple implants extending through cortical regions without increased control overhead in the external head-stage. By observing local field potentials (LFPs) only, chronic stability is improved and good coverage is achieved whilst reducing the spatial density of recording sites. The system features a ∆Σ based instrumentation circuit that digitises high fidelity signal features at the sensor interface thereby minimising analogue resource requirements while maintaining exceptional noise efficiency. This has been implemented in a 0.35 µm CMOS technology allowing for waferscale post-processing for integration of electrodes, RF coil, electronics and packaging within a 3D structure. The presented configuration will record LFPs from 8 electrodes with a 825 Hz bandwidth and an input referred noise figure of 1.77µVrms. The resulting electronics has a core area of 2.1 mm 2 and a power budget of 92 µW.

This paper presents a novel wireless power transfer (WPT) scheme that consists of a two-tier hierarchy of nearfield inductively coupled links to provide efficient power transfer efficiency (PTE) and uniform energy distribution for mm-scale free-positioned neural implants. The top tier facilitates a transcutaneous link from a scalp-worn (cm-scale) primary coil to a subcutaneous array of smaller, parallel-connected secondary coils. These are then wired through the skull to a corresponding set of parallel connected primary coils in the lower tier, placed epidurally. These then inductively couple to freely positioned (mm-scale) secondary coils within each subdural implant. This architecture has three key advantages: (1) the opportunity to achieve efficient energy transfer by utilising two short-distance inductive links; (2) good uniformity of the transdural power distribution through the multiple (redundant) coils; and (3) a reduced risk of infection by maintaining the dura protecting the blood-brain barrier. The functionality of this approach has been verified and optimized through HFSS simulations, to demonstrate the robustness against positional and angular misalignment. The average 11.9% PTE and 26.6% power distribution deviation (PDD) for horizontally positioned Rx coil and average 2.6% PTE and 62.8% power distribution deviation for the vertical Rx coil have been achieved.