Digital approach for the design of statistical analog data acquisition on SoCs

Abstract

With the current demand for mixed-signal SoCs, an increasing number of designers are looking for ADC architectures that can be easily implemented over digital substrates. Since ADC performance is strongly dependent upon physical and electrical features, it gets more difficult for them to benefit from more recent technologies, where these features are more variable. This way, analog signal acquisition is not allowed to follow an evolutionary trend compatible with Moore’s Law. In fact, such trend shall get worst, since newer technologies are expected to have more variable characteristics. Also, for a matter of economy of scale, many times a mixed-signal SoC presents a good amount of idle processing power. In such systems it is advantageous to employ more costly digital signal processing provided that it allows a reduction in the analog area demanded or the use of less expensive analog blocks, able to cope with process variations and uncertainty. Besides the technological concerns, other factors that impact the cost of the design also advise to transfer problems from the analog to the digital domain whenever possible: design automation and self-test requirements, for instance. Recent surveys indicate that the total cost in designer hours for the analog blocks of a mixed-signal system can be up to three times the cost of the digital ones. This manuscript explores the concept of bottom-up analog acquisition design, using statistical sampling as a way to reduce the analog area demanded in the design of ADCs within mixed-signal systems. More particularly, it investigates the possibility of using digital modeling and digital compensation of non-idealities to ease the design of ADCs. The work is developed around three axes: the definition of target applications, the development of digital compensation algorithms and the exploration of architectural possibilities. New methods and architectures are defined and validated. The main notions behind the proposal are analyzed and it is shown that the approach is feasible, opening new paths of future research. Keywords: