Energy-Efficient Integrated Biomedical Circuits and Systems for Unobtrusive Neural Recording and Wireless Body-Area Networks

Despite tremendous progress over the years, current brain-machine interface (BMI) systems are relatively bulky, highly invasive, and limited in their effectiveness except for highly constrained tasks such as moving a cursor on a computer screen. To improve performance of current BMI systems, it is necessary to dramatically increase spatial resolution and coverage across the brain without constraining the mobility of the subject. This calls for innovative approaches to high-density integrated neural recording and stimulation using non-invasive or minimally invasive microelectrode and custom silicon integrated circuits at extreme energy and area efficiency. In this thesis, I present energy-efficient fully integrated miniaturized implants for electrocortical recording and stimulation, and unobtrusive body-area networks systems for subcutaneous power delivery and data communication, as fundamental building blocks to next generation BMI. First I describe a fully wireless, encapsulated neural interface and acquisition chip (ENIAC) in 180nm silicon-on-insulator (SOI) complementary metal-oxide-semiconductor (CMOS) technology for 16-channel neural recording and stimulation including integrated 4x4 electrode array, coil antenna, and wireless power transfer and data telemetry without any external components, completely contained in less than 3mm3 volume suitable for minimally invasive surgical insertion on the cortical surface. A novel fully integrated wireless power receiver design with an RF-decoupled H-tree signal distribution network delivers 1mW power over 1 cm distance while mitigating RF interference in the sensitive analog front-end and acquisition circuits for recording of electrocorticography (ECoG) signals transmitted through the skull. Second I highlight a 1mm2 16-channel neural recording and acquisition system-on-chip in 65nm CMOS offering 92 dB input dynamic range and