This thesis presents the VLSI implementation and characterization of a 256-channel bidirectional integrated neural interface for adaptive cortical stimulation. The microsystem consists of 64 stimulation and 256 recording channels, implemented in a 0.35um CMOS technology with a cell pitch of 200um and total die size of 3.5mm x3.65mm. The stimulator is a current driver with an output current range of 20uA – 250uA. The current memory in every stimulator allows for simultaneous stimulation on multiple active channels. Circuit reuse in the stimulator and utilization of a single DAC yields a compact and low-power implementation. The recording channel has two stages of signal amplification and conditioning and a single-slope ADC. The measured input-referred noise is 7.99uVrms over a 5kHz bandwidth. The total power consumption is 13.3mW. A new approach to CMOS-microelectrode hybrid integration by on-chip Au multi-stud-bumping is also presented. It is validated by in vitro experimental measurements.

This thesis presents the VLSI implementation and characterization of a 256-channel bidirectional integrated neural interface for adaptive cortical stimulation. The microsystem consists of 64 stimulation and 256 recording channels, implemented in a 0.35um CMOS technology with a cell pitch of 200um and total die size of 3.5mm x3.65mm. The stimulator is a current driver with an output current range of 20uA – 250uA. The current memory in every stimulator allows for simultaneous stimulation on multiple active channels. Circuit reuse in the stimulator and utilization of a single DAC yields a compact and low-power implementation. The recording channel has two stages of signal amplification and conditioning and a single-slope ADC. The measured input-referred noise is 7.99uVrms over a 5kHz bandwidth. The total power consumption is 13.3mW. A new approach to CMOS-microelectrode hybrid integration by on-chip Au multi-stud-bumping is also presented. It is validated by in vitro experimental measurements.