TIME-BASED ANALOG-TO-DIGITAL CONVERTERS By DAZHI WEI A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy TIME-BASED ANALOG-TO-DIGITAL CONVERTERS By Dazhi Wei August 2005 Chair: John G. Harris Major Department: Electrical and Computer Engineering We present the concept and some implementations of time-based analog-todigital converters (TB-ADCs). The TB-ADC employs a fundamentally different architecture from the conventional ADC and achieves data conversion by representing signals as a series of discrete time events. This novel architecture trades off simpler analog circuitry on the front end for more complex digital circuitry on the back end, and is very promising for low power applications. We show that theoretically we can use the weighted low-pass kernel (WLPK) method to perfectly reconstruct the signal for the TB-ADCs. This method can also be extended to solve general nonuniform sampling problems. We investigate the effect of different low-pass kernels such as Sinc and Gaussian on the reconstruction performance and computation cost. We also extensively analyze the fundamental performance limitation of the spiking neuron TB-ADC due to nonidealities such as frequency aliasing, leaky integration, thermal noise, signal-dependent threshold variation, and time jitter. Much of this analysis can be extended to other TBADC implementations. We also discuss some other TB-ADC variations including clocked neurons and level-mode TB-ADCs. Test results of several prototype chips