A survey of ADC surveys


Figure 1. Accumulated publication count for scientifically reported ADC implementations in mainstream IEEE sources. The number of publications equivalent to 20% of total is indicated for reference.

SURVEYS GALORE: If the ten posts long A/D-converter survey just concluded did not fully satisfy your desire for scatter plots and tech trends, then this post will provide a list of prior ADC survey works as suggested “further reading”. In fact, I’d recommend everyone serious about data-converter technology trends to get hold of these documents. The list will also serve as a brief history of the ADC survey field. But first some thoughts on surveys:

Survey characteristics

When is it a “survey”? — I’m not going to spend too much energy on a stringent definition of a “survey”. My guideline is that a survey should be based on a significant amount of data, and that the visualization, discussion and interpretation of the data is the main work. Many scientific papers nowadays include a scatter plot that compares a particular design with 5–20 relevant prior efforts. While it’s a good idea to do so, these papers are not considered surveys in this context. Others use large amounts of empirical data to validate or derive a model, but the focus is more on the model.

What is “a significant amount of data”? — The size of the survey should be related to the total amount of data available at the time of the survey. A survey of 200 papers would have been exhaustive in 1990. Today it represents less than 12% of all scientific publications. The accumulated amount of scientific papers over time is shown in Fig. 1. While this is not the absolute total number of ADC publications, it covers the ADC implementations reported in nearly all journals and conferences central to the A/D-converter field, and shall for simplicity be referred to as the “total” amount here. The number of sources equivalent to 20% of the accumulated total at any given time is also shown in Fig. 1.

So, how much of the total do I need? Well, it depends on what you’re trying to do. When it comes to survey data, I’m a firm believer in “the more the merrier”, but there are tasks which can be done with a fairly small subset. For example, if you want to get an idea of the overall trend for one parameter vs. another or just make a quick sanity check.

Small subsets do have some limitations though. For the subset to function as a reasonably generic approximation of the exhaustive set, its data must span roughly the same chunk of parameter space, and have similar distribution of values in all dimensions. This is difficult to achieve unless you make a random sampling of the exhaustive set. A smaller set also risks running out of data, for example when dividing it further according to some parameter such as resolution or architecture.

How quickly will a survey become dated? — I really don’t know. I guess it depends on what you wish to study. But we can observe, as in Fig. 2, how the accumulated total at any given time relates to the overall total (here 1708 papers), and what percentage of currently available works were yet unpublished at any given time (e.g., at the end year of a particular survey). It is seen that approximately 50% of all currently available papers (~Q1-2012) were published in the last 8–8.5 years, i.e., after 2003, and almost 30% were not yet published in 2007. By the end of 1997, 70% of today’s body of empirical data was still unpublished.

You can use Fig. 2 to assess how old a survey can be before it’s no longer useful for your purpose. Can you make business decisions based on trend estimates where the most recent half of the data set is missing? Probably not. Most recent 30/20/10%? If so, you need a survey that’s less than approximately 4/3/1 years old.

It is clear that continuously updated surveys, such as Murmann’s, or the one used here at Converter Passion are preferable over single-shot attempts, since the former allows for continuously updated trend estimations.

Figure 2. Paper “yield”. The fraction of current total already published (blue) and yet to be published (red) at the end of any given year.

Known ADC surveys

Author Size Years Type What Ref
Walden 100 ≤ 1994 Both Perf. limits, FOM [1]
Walden 150 1978-1997 Both Perf. limits, FOM, jitter, evolution [2]
Merkel 150 1993-2002 Both Perf. limits, SFDR, power, VDD, scaling, device, arch. [3]
Le 1000 1983-2004 Parts Perf. limits, jitter, cost, arch., no. chan, N [4]
Walden 175 1978-2007 Both Update++ [5]
Walden n/a 1978-2008 Both Update [6]
Murmann ~260 1997-2008 Sci Perf. limits, VDD, scaling, FOM, evolution [7]
Jonsson 1400 1974-2010 Sci Perf., VDD, scaling, FOM, evolution [8]
Jonsson 1100 1976-2010 Sci Perf. & FOM vs. CMOS scaling, evolution [9]
Fuiano 5540 1970-2010 Sci Data-converters, research/patent correlation [10]
Jonsson 1400 1974-2010 Sci Energy/sample by arch [11]
Jonsson 1500 1974-2011 Sci Area eff by arch [12]
Murmann ~350 1997-2012 Sci Online survey data [13]
Jonsson 1700 1974-2012 Sci Perf., VDD, scaling, jitter, SFDR, FOM, evolution [14]

About the surveys

Walden

The “mother of all ADC surveys”, and the most frequently cited of all, is the pioneering work by Walden [2] where 150 scientific and commercial ADCs were analyzed, and performance trends were extracted. An earlier version was published already in 1994 [1], but this extended work became “The Walden Survey” to most of us. Although the 150 source documents originated from a mix of commercial and experimental designs, the Walden survey had a size equivalent to 30% of all scientific publications available at the time. The methods introduced in [2] are still useful, but Fig. 1 and Fig. 2 suggest that the trends extracted in [2] are unlikely to be valid and applicable today. At least they would have to be confirmed using more recent data. Two updated versions of the survey were published in 2008 – one covering 175 ADCs and data until 2007 [5], and one with an unspecified survey size and data until 2008 [6]. It is unclear how the 175 converters included in [5] were selected. During the time from Walden’s classic survey to 2007, the academic output alone generated another 715 new sources – commercial parts not counted. The +25 increase in source data therefore seems surprisingly incremental. Still, some of the results in [5] align very well with Converter Passion data, so apparently it was a carefully chosen subset.

Merkel & Wilson

Merkel and Wilson surveyed 150 commercial and scientific ADCs with specifications suitable for defense space applications [3]. Their data appear to span from 1993–2002, and the selection criteria for inclusion in the survey was a sampling rate fs ≥ 1 MS/s, and nominal resolution ≥ 12 bits. The paper does not reveal the mix between scientific papers and commercial parts, but gathering 150 sources must have been quite an effort by the authors. The total scientific output matching these specs and the time period is no more than 81 papers, and only 59 in the two sources (ISSCC, JSSC) the authors mention as primary. An additional minimum of 69–91 commercial parts must have been included to reach 150 sources. It is therefore assumed that the Merkel & Wilson data set was close to exhaustive for the spec range surveyed, and exhaustive data sets are always applauded here at Converter Passion.

The analysis and discussion itself is geared towards the stated application and focused on linearity (SFDR) to the extent that noise parameters are not treated at all. Power dissipation, supply voltage, speed, device type, scaling and architecture were observed.

Le, Rondeau, Reed & Bostian

An enormous data set, covering nearly 1000 commercial ADC parts from 1983–2004 was used in the survey by Le, Rondeau, Reed and Bostian [4]. As a comparison, the scientific output from the same years (not included in their survey) is 900 papers. The work is firmly rooted in the Walden tradition, but also considers parameters such as the number of channels per package and cost vs. performance. Additionally, the treatment separates the data by architecture, which adds an interesting extra dimension. Because of the larger volume and time span of the data set, part of the focus is to establish differences between this work and the classic Walden paper. Unfortunately, some exponentially improving parameters were plotted along linear axes, which makes many results from the survey difficult to see or interpret. Nevertheless, the contribution by Le et al. is a gigantic work and a key reference.

Murmann

The survey by Murmann [7] is a significant recent contribution to the analysis of empirical performance data. It covers approximately 260 scientific ADCs reported 1997–2008 at the two conferences VLSI Circuit Symposium and ISSCC. The work analyzes ADC performance trends with a focus on energy per sample and signal-to-noise-and-distortion ratio (SNDR). The impact of process and voltage scaling is considered. If you don’t have this paper already, you should definitely head over to IEEE Xplore and get it right now.

Murmann’s survey has further benefits in that it is continuously updated and the data set is available online [13]. The latter opens up a lot of possibilities for anyone wishing to analyze the data in their own way, and makes the survey a very important contribution to the field. It currently includes around 350 sources.

Fuiano, Cagnazzo & Carbone

A rather different angle is taken in [10], where Fuiano, Cagnazzo and Carbone use survey data to analyze the correlation between scientific literature and patent activity. Compared to more “Waldenesque” surveys, this is a rather different animal. It nevertheless appeals to me as it illustrates an attempt to mine large amounts of survey data for something more unusual than ENOB, fs and FOM.

Jonsson

The ADMS Design data set used here at Converter Passion has also been used in five scientific papers, of which four are “surveys”:

  • ADC trends and performance evolution over time was analyzed in [8].
  • The impact of CMOS scaling on ADC performance was empirically analyzed in [9].
  • ADC architectures were compared with respect to energy efficiency in [11].
  • Area-efficiency of ADC architectures was surveyed in [12].

The largest survey for which this data set has been used so far is the recently published series of posts on A/D-converter performance evolution [14].

Other survey-related literature

A few other prior publications that are “survey-ish”, or otherwise use a large set of empirical data for their analysis are listed here:

  • Vogels and Gielen used a multidimensional regression fit to derive an ADC power dissipation model/FOM based on ≥ 70 empirical data points divided by architecture [15]. A similar approach was recently used by Verhelst and Murmann to analyze power dissipation and area vs. scaling based on Murmann’s data set [16] .
  • Sundström, Murmann, and Svensson derived theoretical power dissipation bounds in [17], and used the Murmann set to compare theory with empirical reality.
  • In [18], it was illustrated how the quality of a figure-of-merit (FOM) can be assessed by testing it against a large set of empirical data.

If you feel that I’ve left out any contributions that could have been mentioned in this post, just add a comment below.

See also …

ADC survey data

A/D-converter performance evolution

EveryNano Counts: “Those ADC Literature Surveys”

References

  1. R. H. Walden, “Analog-to-digital converter technology comparison,” in Proc. of GaAs IC Symp., pp. 228–231, Oct., 1994.
  2. R. H. Walden, “Analog-to-digital converter survey and analysis,” IEEE J. Selected Areas in Communications, no. 4, pp. 539–550, Apr. 1999.
  3. K. G. Merkel, and A. L. Wilson, “A survey of high performance analog-to-digital converters for defense space applications,” in Proc. IEEE Aerospace Conf., Big Sky, Montana, Mar. 2003, vol. 5, pp. 2415–2427.
  4. B. Le, T. W. Rondeau, J. H. Reed, and C. W. Bostian, “Analog-to-digital converters [A review of the past, present, and future],” IEEE Signal Processing Magazine, pp. 69–77, Nov. 2005.
  5. R. Walden, “Analog-to-digital conversion in the early twenty-first century,” Wiley Encyclopedia of Computer Science and Engineering, pp. 126–138, Wiley, 2008.
  6. R. H. Walden, “Analog-to-digital converters and associated IC technologies,” in Proc. Compound Semiconductor Integrated Circuits Symp., Monterey, Oct. 2008, pp. 1–2.
  7. B. Murmann, “A/D converter trends: Power dissipation, scaling and digitally assisted architectures,” Proc. of IEEE Custom Integrated Circ. Conf. (CICC), San Jose, California, USA, pp. 105–112, Sept., 2008.
  8. B. E. Jonsson, “A survey of A/D-converter performance evolution,” Proc. of IEEE Int. Conf. Electronics Circ. Syst. (ICECS), Athens, Greece, pp. 768–771, Dec., 2010.
  9. B. E. Jonsson, “On CMOS scaling and A/D-converter performance,” Proc. of NORCHIP, Tampere, Finland, Nov. 2010.
  10. F. Fuiano, L. Cagnazzo, and P. Carbone, “Data Converters: an Empirical Research on the Correlation between Scientific Literature and Patenting Activity,” Proc. of 2011 IMEKO IWADC & IEEE ADC Forum, Orvieto, Italy, pp. 1–6, June, 2011.
  11. B. E. Jonsson, “An empirical approach to finding energy efficient ADC architectures,” Proc. of 2011 IMEKO IWADC & IEEE ADC Forum, Orvieto, Italy, pp. 1–6, June 2011.
  12. B. E. Jonsson, “Area Efficiency of ADC Architectures,” Proc. of Eur. Conf. Circuit Theory and Design (ECCTD), Linköping, Sweden, pp. 560–563, Aug., 2011.
  13. B. Murmann, “ADC Performance Survey 1997-2012,” [Online]. Available: http://www.stanford.edu/~murmann/adcsurvey.html.
  14. B. E. Jonsson, “A/D-converter Performance Evolution,” Converter Passion, Aug., 2012, Available: https://converterpassion.wordpress.com/articles/ad-converter-performance-evolution/.
  15. M. Vogels, and G. Gielen, “Architectural Selection of A/D Converters,” Proc. of Des. Aut. Conf. (DAC), Anaheim, California, USA, pp. 974–977, June, 2003.
  16. M. Verhelst, and B. Murmann, “Area scaling analysis of CMOS ADCs,” El. Letters, Vol. 48, No. 6, pp. 315–315, Mar., 2012, IEE.
  17. T. Sundström, B. Murmann, and C. Svensson, “Power dissipation bounds for high-speed Nyquist analog-to-digital converters,” IEEE Trans. Circuits and Systems, pt. I, vol. 56, no. 3, pp. 509–518, Mar. 2009.
  18. B. E. Jonsson, “Using Figures-of-Merit to Evaluate Measured A/D-Converter Performance,” Proc. of 2011 IMEKO IWADC & IEEE ADC Forum, Orvieto, Italy, pp. 1–6, June 2011.
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4 responses to “A survey of ADC surveys

  1. another little ADC performance trend
    “Understanding State of the Art in ADCs”
    © 2008 Brad Brannon, Analog Devices, Inc.
    http://www.converter-radio.com/understandingSOA.pdf

  2. Mr. Jonsson, Can you give us a (Classic and emerging) ADC architecture evolution trend survey?
    Dr. Un-ku Moon of Oregon State University recently talked about some interesting “Emerging ADCs”。

    another suggestion :
    Your Converter Passion is very wonderful, but all about ADCs!, Can you give us some DACs performance trends survey?

    • Thank you again! Yes, I’m more than slightly biased towards ADC, but a DAC survey is actually in the works. Stay around, and ye shall receive …

      The first part sounds challenging. I’ll have to check what Dr. Moon refers to as emerging [you know already, I guess], and then see if there’s any angle on it relating to empirical data. Some architecture trends could always be an option, of course.

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