Statistical ADC enhanced by pipelining and subranging

Sen Tao, Emmanuel Abbe, Naveen Verma

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


This brief explores the potential for enhancing both the achievable dynamic range and energy efficiency of statistical analog-to-digital converters (ADCs). To address dynamic range, the focus is on 1) the use of a strong kernel for statistical estimation (maximum-likelihood estimator) and 2) enabling the use of a large number of statistical observations. However, such a kernel, when used with a large number of observations, imposes high complexity and energy cost. To address energy, a pipelined front-end estimator is employed for coarse subrange estimation, and a back-end estimator is employed for fine statistical estimation. Architectural optimization of the back-end and front-end estimators is presented. For an ADC with nominal resolution of 10 bits, the approach achieves < 1.3 LSB root-mean-square error, while reducing computations by 15× compared to full statistical estimation.

Original languageEnglish (US)
Article number7047826
Pages (from-to)538-542
Number of pages5
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Issue number6
StatePublished - Jun 1 2015

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


  • Analog-to-digital converter
  • comparators
  • statistical architecture


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