An ultrasound powering TX ASIC for brain implants with autonomous on-chip standing-wave peak tracking for RX power (PDL) regulation is presented. With a proposed adiabatic power-sensing scheme, the TX consumes 43μW for power tracking, and the system achieves a settling time of <90ms while using the standing-wave peak-tracking FSM. The TX can achieve PDL improvement of up to 2.7× with a power-tracking accuracy of 82%.

State-of-the-art intracortical neural recording and stimulation systems rely on subdural implants tethered to a cranial implant which itself has a wireless power and data link to the outside world [1] (Fig. 6.2.1). However, this tethered configuration poses challenges such as scarring and potential damage to the surrounding tissue due to strain and micromotions, making this approach unsuitable for chronical implants [2]. Consequently, there is growing interest in wireless connections between cranial and subdural implants. This paper focuses on wireless powering between implants, traversing the dura and cerebrospinal fluid (CSF) tissue layers over distances of 0.5 to 1cm (transdural powering). With modern burr-hole craniotomy, the hole drilled in the skull is 6mm in diameter, limiting the available size for the TX. Moreover, the power dissipation of the TX must be low to keep tissue heating below 1°C [3]. RF and optical modalities suffer from higher attenuation in tissue compared to ultrasound (0.6dB/cm/MHz) [4]. Furthermore, for transdural powering, power losses from reflections at medium interfaces (e.g., skull) are avoided, making ultrasound (US) a prime candidate for efficient in-body wireless power transfer. US is also preferable to inductive powering since US beam steering up to large angles (>45°) is needed to maximize power delivery and compensate for brain micromotions of up to ±4mm [5] and misalignment during surgery. However, prior art US driving systems either use single-phase transducer driving [6, 7], incapable of beam steering, or use class D drivers with low power transfer efficiency (PTE) [8, 9]. A phased array with increased driving efficiency was presented in [10], but it cannot perform beam steering without grating lobes that can be eliminated with miniature transducers with a pitch close to λ/2. To facilitate direct integration between CMOS and the transducer array, the CMOS driving units should also be pitch matched [8, 9].

Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge owing to the constrained operational environment, which requires low latency and high energy efficacy. Previous benchmarks have provided limited insights into power consumption and latency. However, this study introduces algorithmic metrics that capture the potential and limitations of neural decoders for closed-loop intra-cortical brain-computer interfaces in the context of energy and hardware constraints. This study benchmarks common decoding methods for predicting a primate’s finger kinematics from the motor cortex and explores their suitability for low latency and high energy efficient neural decoding. The study found that ANN-based decoders provide superior decoding accuracy, requiring high latency and many operations to effectively decode neural signals. Spiking neural networks (SNNs) have emerged as a solution, bridging this gap by achieving competitive decoding performance within sub-10 ms while utilizing a fraction of computational resources. These distinctive advantages of neuromorphic SNNs make them highly suitable for the challenging closed-loop neural modulation environment. Their capacity to balance decoding accuracy and operational efficiency offers immense potential in reshaping the landscape of neural decoders, fostering greater understanding, and opening new frontiers in closed-loop intra-cortical human-machine interaction.