UCLA

Biomedical implant-scale electronics have gained a lot of attention in recent years. Particularly, neuromodulation implants are an important tool in treating drug-resistant neurological conditions, while also improving our understanding of the brain. Although demands for adding more functionality to the implant are constantly increasing, their power consumption and size are usually limiting factors that determine longevity of the battery and dictates the overall throughput of brain data. Therefore, in order to gain more insight into brain dynamics while keeping device small, it is crucial to increase number of accessing channels and to improve the overall device efficiency.

Enabling better platform technologies that would greatly impact the field of neuroscience and enhance the quality of life of patients with neurological disorders is a difficult task. This work seeks to address some of the design challenges related to a variety of biomedical applications, while providing the power efficiency and flexibility needed for implantable devices.

First, a new self-powered, thermo-electric harvesting architecture is proposed and demonstrated. The miniaturized system, accompanied with efficient energy processing circuits was able to achieve a cold startup with a few 10's of mV of input voltage while achieving good end-to-end efficiency. This design was further verified in real environment (in-vivo, rat) and showed a good trade-off between the form factor and extracted power.

Second, we demonstrated a 'holy grail' implant-scale neuromodulation interface with high linear input range that enables concurrent sensing and stimulation. Our 64-channel interface meets the requirements of human-quality implants at an unprecedented level of electronic miniaturization as compared to prior art. It offers major new clinical perspectives: it supports different power delivery options, always-on sensing for enhanced closed-loop therapy, multi-channel arbitrary stimulation waveforms with user-friendly programming, high-resolution neural interface for more precise target localization.

Finally, a new neural recording paradigm based on the fast calcium imaging is described. This technology can provide communication between the brain and the external world at the resolution of individual neurons. We propose a hardware friendly approach for analyzing 1000's of neurons in a single pipeline and in real-time, while relaxing the memory and computational requirements. This method is capable of delivering two orders of magnitude higher brain coverage as compared to the state-of-the-art electrophysiological approach, leading to a high-resolution, high-data-rate neural interface.