Adaptation of log domain second order filters implemented by floating gate MOSFETs

We describe an adaptive log domain second order filter with integrated learning rules. The system is implemented using multiple input floating gate transistors to realize online learning of quality factor and time constant. We use adaptive dynamical system theory to derive robust control laws for both quality factor and time constant adaptation in a system identification task. The log domain filters adapt to estimate the quality factor and time constant of a reference filter accurately and efficiently as the parameters of the reference are changed. We present simulation results for 0.5mum technology which demonstrate that adaptation occurs within milliseconds. The filter consumes 2muW and the entire system consumes 10muW