Adaptive log domain filters using floating gate transistors

We present an adaptive log domain filter with integrated learning rules for model reference estimation. The system is a first order low pass filter based on a log domain topology that incorporates multiple input floating gate transistors to implement on-line learning of gain and time constant. Adaptive dynamical system theory is used to derive robust learning rules for both gain and timeconstant adaptation in a system identification task. The adaptive log domain filters have simulated cutoff frequencies above 100kHz with power consumption of 23μW and show robust adaptation of the estimated gain and time constant as the parameters of the reference filter are changed.