Abstract:
I will present several unconventional data conversion architectures.
First, I will talk about how we can make use of noise, which is usually deemed as an undesirable thing, to estimate the conversion residue and increase the SNR of a SAR ADC. It is an interesting example of stochastic resonance, in which the presence of noise can lead to not SNR degradation but SNR enhancement. Second, I will talk about how we can perform data conversion below the Nyquist rate by exploiting the sparsity of the input signal. I will show two example compressive sensing ADCs and how the effective ADC conversion rate can be reduced by 4 times but without losing information. Third, I will show how we can prevent the seemingly inevitable kT/C noise in a Nyquist-rate pipelined ADC by using a continuous-time SAR based 1st-stage. This can substantially reduce the requirement on the ADC input capacitance, greatly reducing the ADC driver power and reference buffer power.

Bio:
Nan Sun is Associate Professor at the University of Texas at Austin.
He received the B.S. degree from Tsinghua University in 2006, where he ranked top and graduated with the highest honor. He received the Ph.D.
degree from Harvard University in 2010. Dr. Sun received the NSF Career Award in 2013. He serves on the Technical Program Committee of the IEEE Custom Integrated Circuits Conference and the IEEE Asian Solid-State Circuit Conference. He is an Associate Editor of the IEEE Transactions on Circuits and Systems - I: Regular Papers, and a Guest Editor of the IEEE Journal of Solid-State Circuits. He also serves as IEEE Circuits- and-Systems Society Distinguished Lecturer from 2019 to 2020.