A great demand exists for technologies that enable neuroscientists and clinicians to simultaneously observe the activity of many neurons in the brain. By recording this activity, awake animal behavior can be predicted in real time, brain-machine interfaces controlling the machine by thought can be set up, and treatments for neurological disorders can be explored. Existing commercial neural recording equipment are bench-top systems that are bulky, high cost, and consume high power. They also require wire bundles tethering the neural recording probes to skull-mounted a connector that lead to tissue infection, external noise and interfering signals coupling. To overcome these disadvantages, a miniature wireless implanted multi-channel integrated neural recording micro-system with low power and low noise is needed. This thesis contributes to the analog front end of such a micro-system, which provides a low-power, low-noise neural interface that detects and amplifies neural signals and digitizes them for further signal processing. The front end includes neural amplifiers and an analog-to-digital convertor (ADC). This thesis work addresses the challenges to developing an analog front end for wireless implanted multi-channel neural recording systems, which include ultra low noise, extremely low power, high power supply rejection radio, low area occupation, sufficient data conversion speed and optimizing design tradeoff between all these requirements. Two versions of a neural amplifier were built. The second version was optimized based on the design experience of the first version and a comprehensive theoretical analysis of neural amplifiers. Following the optimization guidelines, noise efficiency and a new figure of merit for neural amplifiers were effectively improved. A successive approximation (SAR) ADC tailored to wireless implantable neural recording systems was also designed. The new SARADC is able to process 32 neural spikes recording channels in a multiplexing manner with low power consumption and low area occupation. The results of this research lay a solid foundation for future realization of high sensitivity wireless implantable neural recording system.
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