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Repository: bmurmann/ADC-survey
Branch: main
Commit: 3f8d5ea887dd
Files: 17
Total size: 492.4 KB
Directory structure:
gitextract_e0xcdi_y/
├── LICENSE
├── README.md
├── pdf/
│ └── README.md
├── plots/
│ ├── README.md
│ ├── aperture_plot.ipynb
│ ├── aperture_trend_plot.ipynb
│ ├── energy_plot.ipynb
│ ├── foms_plot.ipynb
│ ├── foms_plot.json
│ └── foms_trend_plot.ipynb
└── xls/
├── ADCsurvey_latest.ods
├── ADCsurvey_latest.xlsx
├── ADCsurvey_rev20240808.xlsx
├── ADCsurvey_rev20250609.xlsx
├── ADCsurvey_rev20250721.xlsx
├── ADCsurvey_rev20260314.xlsx
└── README.md
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FILE CONTENTS
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FILE: LICENSE
================================================
BSD 3-Clause License
Copyright (c) 2022, Boris Murmann
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
================================================
FILE: README.md
================================================
# ADC Performance Survey
*Data collection from the ISSCC & VLSI Circuit Symposium, 1997-2026*
For use in publications and presentations please cite as follows:
B. Murmann, "ADC Performance Survey 1997-2026," [Online]. Available: https://github.com/bmurmann/ADC-survey.
```
@misc{adc_survey,
author = {Murmann, Boris},
title = {{ADC Performance Survey 1997-2026}},
note = {[Online]. Available: \url{https://github.com/bmurmann/ADC-survey}}
}
```
**Related Material**
* B. Murmann, "Introduction to ADCs/DACs: Metrics, Topologies, Trade Space, and Applications," ISSCC Short Course Presentation, Feb. 2022. [PDF](https://github.com/bmurmann/ADC-survey/blob/69cb1e4e819c9e0222cfbd386968d1f24ade16fa/pdf/ISSCC2022-Short%20Course-Murmann.pdf)
* B. Murmann, M. Verhelst, and Y. Manoli, “Analog-to-Information Conversion,” in NANO-CHIPS 2030, by B. Murmann and B. Hoefflinger (eds.), Springer, 2020. [DOI](http://dx.doi.org/10.1007/978-3-030-18338-7_17)
* B. Murmann, “The successive approximation register ADC: a versatile building block for ultra-low-power to ultra-high-speed applications,” in IEEE Communications Magazine, vol. 54, no. 4, pp. 78-83, Apr. 2016. [DOI](http://dx.doi.org/10.1109/MCOM.2016.7452270)
* M. Keller, B. Murmann, and Y. Manoli, “Analog-Digital Interfaces—Review and Current Trends,” in CHIPS 2020 VOL. 2, by B. Hoefflinger (ed.), Springer, 2016. [DOI](https://doi.org/10.1007/978-3-319-22093-2_4)
* B. Murmann, “The Race for the Extra Decibel: A Brief Review of Current ADC Performance Trajectories,” IEEE Solid-State Circuits Magazine, vol. 7, no. 3, pp. 58-66, 2015. [DOI](http://dx.doi.org/10.1109/MSSC.2015.2442393)
* B. Murmann, "A/D Converter Figures of Merit and Performance Trends," ISSCCx: Circuit and System Insights, Feb. 2015. [YouTube](https://www.youtube.com/watch?v=dlD0Jz3d594)
* B. Murmann, “Digitally Assisted Data Converter Design,” (Keynote Paper) Proc. ESSCIRC, Bucharest, Romania, Sep. 2013, pp. 24-31. [DOI](http://dx.doi.org/10.1109/ESSCIRC.2013.6649063)
* B. Murmann, “Energy limits in A/D converters,” (Keynote Paper) IEEE Faible Tension Faible Consommation (FTFC), Paris, France, Jun. 2013. [DOI](http://dx.doi.org/10.1109/FTFC.2013.6577781)
* M. Verhelst and B. Murmann, “Area scaling analysis of CMOS ADCs,” Electronics Letters, vol. 48, no. 6, pp. 314-315, Mar. 15 2012. [DOI](http://dx.doi.org/10.1049/el.2012.0253)
* B. Murmann, “A/D converter trends: Power dissipation, scaling and digitally assisted architectures,” in Proc. IEEE Custom Integrated Circuits Conf., San Jose, CA, Sep. 2008, pp. 105-112. [DOI](http://dx.doi.org/10.1109/CICC.2008.4672032)
* B. Murmann, “Limits on ADC Power Dissipation,” in Analog Circuit Design, by M. Steyaert, A.H.M. Roermund, J.H. van Huijsing (eds.), Springer, 2006. [DOI](https://doi.org/10.1007/1-4020-3885-2_16)
================================================
FILE: pdf/README.md
================================================
Various resources in PDF format.
================================================
FILE: plots/README.md
================================================
This directory provides some examples for plotting the ADC survey data. The Jupyter notebooks can be opened and run in Google Colab (no local Jupyter installation needed). You can click the examples below to download high-resolution png files.
**Aperture Plot**: Signal-to-noise and distortion ratio (SNDR) versus input frequency (f<sub>in,hf</sub>). For these data points, the input frequency is typically chosen as the highest reported, often near f<sub>s</sub>/2. In this case, high-speed converters are often limited by clock jitter, and it is reasonable to use the measured SNDR as a proxy for the SNR caused by jitter alone. We see that today's best designs get close to an equivalent jitter of approximately 50 fs.
<img src="aperture_plot.png" width="400" />
**Aperture Trend Plot**: The plot below shows the progression of the equivalent jitter over time. The best 3 designs to date are averaged for each data point. The value of 50 fs was reached in the late 2010s but hasn't progressed much since. Generating and distributing a sampling clock with significantly lower jitter remains a grand challenge for A/D interfaces.
<img src="aperture_trend_plot.png" width="400" />
**Energy Plot**: A/D conversion energy (power/sampling rate) versus signal-to-noise and distortion ratio (SNDR). Designs with high SNDR are typically limited by thermal noise and we expect the energy to quadruple per added bit. The data confirms this with a good fit to 4<sup>ENOB</sup> (where ENOB=SNDR-1.76/6.02) for high SNDR. For low SNDR, the energy tends to plateau at some level set by CV<sup>2</sup> limits (as opposed to energy invested to overcome noise). Process technology scaling and reduced supply voltages can help with lowering the horizontal asymptote. The colors of each point give an indication of the conversion speed. Low-speed designs tend to have lower energy at a given SNDR. The lowest absolute energy points on this chart correspond (not surprisingly) to ADCs that have neither high resolution nor high speed.
<img src="energy_plot.png" width="400" />
**Figure of Merit Plot**: The above Energy Plot indicates that high-speed ADCs tend to be less energy efficient. To visualize this tradeoff more directly, we can lump the relevant metrics into a figure of merit (FOM). Shown below is a plot of the SNDR-based Schreier FOM ($FOM_S=SNDR(dB)+10log(f_{snyq}/2/P)$) versus sampling rate. (Larger is better in this plot.) FOM<sub>S</sub> conforms with the asymptotic cost of 4x in power per 6 dB of SNDR increase, seen in the above Energy Plot. It also assumes that power scales linearly with conversion speed. The plot shows that this latter assumption breaks down at some corner frequency (near 100 MHz), beyond which the FOM drops by about 10 dB per decade. This suggests a quadratic relationship between power and conversion speed in this high-speed region. Using the envelope data of this plot, one can estimate the power dissipation of a state-of-the-art ADC for a given SNDR requirement and conversion speed. Try it out using this [calculator](https://murmann-group.github.io/adc_survey/).
<img src="foms_plot.png" width="400" />
**Figure of Merit Trend Plot**: The plot below shows the improvement in $FOM_S$ over time. The best 5 designs to date are averaged for each data point. The blue line is for the horizontal (low-frequency) asymptote in the Figure of Merit Plot. The red curve is for the high-frequency asymptote, evaluated at 1 GHz. We observe that low-speed designs are just a few decibels away from the classical $8kT$ x $SNR$ limit for an ideal class-B analog circuit. The $FOM_S$ improvement rate for both high- and low-speed designs has been in the range of 1-2 dB.
<img src="foms_trend_plot.png" width="400" />
================================================
FILE: plots/aperture_plot.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"<a href=\"https://colab.research.google.com/github/bmurmann/ADC-survey/blob/main/plots/aperture_plot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MOkmxjrkkPJS"
},
"source": [
"# ADC Survey: Aperture Plot"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "h4nZaDktMRTL"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"try:\n",
" from google.colab import files\n",
" IN_COLAB = True\n",
"except:\n",
" IN_COLAB = False"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "U6pyV95WkVGx"
},
"outputs": [],
"source": [
"# Read latest survey data and concatenate ISSCC and VLSI data\n",
"if IN_COLAB:\n",
" data = \"https://github.com/bmurmann/ADC-survey/blob/main/xls/ADCsurvey_latest.xlsx?raw=true\"\n",
"else:\n",
" data = \"../xls/ADCsurvey_latest.xlsx\"\n",
"df1 = pd.read_excel(data, sheet_name='ISSCC')\n",
"df2 = pd.read_excel(data, sheet_name='VLSI')\n",
"df = pd.concat([df1, df2])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VxF-jvN0rEBe",
"outputId": "4be5e2fd-f2e4-45bd-e06e-9afa2b02ff2b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['YEAR', 'ID', 'TYPE', 'ARCHITECTURE', 'TECHNOLOGY', 'TITLE', 'ABSTRACT',\n",
" 'AUTHORS', 'DOI', 'LINK', 'COMMENTS', 'VSUP1 [V]', 'VSUP2 [V]',\n",
" 'VSUP3 [V]', 'Csamp [pF]', 'AREA [mm^2]', 'SNDR_lf [dB]', 'fin_hf [Hz]',\n",
" 'SNDR_hf [dB]', 'SNR [dB]', 'DR [dB]', '-THD [dB]', 'SFDR [dB]',\n",
" 'SNDR_plot [dB]', 'P [W]', 'fs [Hz]', 'OSR', 'fsnyq [Hz]',\n",
" 'fcenter [Hz]', 'P/fsnyq [pJ]', 'FOMW_lf [fJ/conv-step]',\n",
" 'FOMW_hf [fJ/conv-step]', 'FOMS_lf [dB]', 'FOMS_hf [dB]',\n",
" 'FOMW_hf/fsnyq', 'FOMS,hf+10log(fsnyq)'],\n",
" dtype='object')\n"
]
}
],
"source": [
"# Show headers of data table\n",
"print(df.keys())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "snl3YeC_ge0g"
},
"outputs": [],
"source": [
"# Compute envelope for aperture plot\n",
"jitter_estimate = 1/(2*np.pi*df['fin_hf [Hz]'] * np.sqrt(10**(df['SNDR_hf [dB]']/10)))\n",
"smallest_three = sorted(jitter_estimate, key = lambda x : float('inf') if np.isnan(x) else x)[:3]\n",
"jitter_min = np.mean(smallest_three)\n",
"jitter_max = 1e-12\n",
"env_x = np.linspace(1e6, 1e11, 100)\n",
"env_y1 = -20*np.log10(2*np.pi*env_x*jitter_min)\n",
"env_y2 = -20*np.log10(2*np.pi*env_x*jitter_max)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "d5JmQy4ucGeD"
},
"outputs": [],
"source": [
"# Decent settings for a PowerPoint figure\n",
"font=16\n",
"size=(8, 5)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 507
},
"id": "0v3-8r5ooVn-",
"outputId": "eaec4c5b-6e89-4f25-8c3d-f64eb7380914"
},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=size)\n",
"ax.plot(df['fin_hf [Hz]'], df['SNDR_hf [dB]'], 'b.', ms=15)\n",
"ax.plot(env_x, env_y1, 'k', label='Jitter=%.1f fs' %(jitter_min/1e-15))\n",
"ax.plot(env_x, env_y2, 'k--', label='Jitter=%.0f fs' %(jitter_max/1e-15))\n",
"ax.legend(frameon=1, fontsize=font-3)\n",
"ax.set_xscale('log')\n",
"ax.set_xlim((1e6, 1e11))\n",
"ax.set_ylim((20, 100))\n",
"plt.xticks(fontsize=font)\n",
"plt.yticks(fontsize=font)\n",
"plt.xlabel('$f_{in,hf}$ [Hz]', fontsize=font)\n",
"plt.ylabel('$SNDR$ [dB]', fontsize=font)\n",
"plt.title('ISSCC & VLSI 1997-%d' %max(df['YEAR']))\n",
"plt.grid(True)\n",
"fig.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"id": "F6V04XwJMSxZ",
"outputId": "61a6cb69-0515-4322-9924-3bf389a0122a"
},
"outputs": [],
"source": [
"# save as high-resolution png file\n",
"if IN_COLAB:\n",
" files.download('aperture_plot.png')\n",
"else:\n",
" fig.savefig(\"aperture_plot.png\", dpi=600)"
]
}
],
"metadata": {
"colab": {
"authorship_tag": "ABX9TyNFi75FryQZLjLMzbsULMTI",
"include_colab_link": true,
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: plots/aperture_trend_plot.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"<a href=\"https://colab.research.google.com/github/bmurmann/ADC-survey/blob/main/plots/aperture_plot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MOkmxjrkkPJS"
},
"source": [
"# ADC Survey: Aperture Trend Plot"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"id": "h4nZaDktMRTL"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"try:\n",
" from google.colab import files\n",
" IN_COLAB = True\n",
"except:\n",
" IN_COLAB = False"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"id": "U6pyV95WkVGx"
},
"outputs": [],
"source": [
"# Read latest survey data and concatenate ISSCC and VLSI data\n",
"if IN_COLAB:\n",
" data = \"https://github.com/bmurmann/ADC-survey/blob/main/xls/ADCsurvey_latest.xlsx?raw=true\"\n",
"else:\n",
" data = \"../xls/ADCsurvey_latest.xlsx\"\n",
"df1 = pd.read_excel(data, sheet_name='ISSCC')\n",
"df2 = pd.read_excel(data, sheet_name='VLSI')\n",
"df = pd.concat([df1, df2])"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VxF-jvN0rEBe",
"outputId": "4be5e2fd-f2e4-45bd-e06e-9afa2b02ff2b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['YEAR', 'ID', 'TYPE', 'ARCHITECTURE', 'TECHNOLOGY', 'TITLE', 'ABSTRACT',\n",
" 'AUTHORS', 'DOI', 'LINK', 'COMMENTS', 'VSUP1 [V]', 'VSUP2 [V]',\n",
" 'VSUP3 [V]', 'Csamp [pF]', 'AREA [mm^2]', 'SNDR_lf [dB]', 'fin_hf [Hz]',\n",
" 'SNDR_hf [dB]', 'SNR [dB]', 'DR [dB]', '-THD [dB]', 'SFDR [dB]',\n",
" 'SNDR_plot [dB]', 'P [W]', 'fs [Hz]', 'OSR', 'fsnyq [Hz]',\n",
" 'fcenter [Hz]', 'P/fsnyq [pJ]', 'FOMW_lf [fJ/conv-step]',\n",
" 'FOMW_hf [fJ/conv-step]', 'FOMS_lf [dB]', 'FOMS_hf [dB]',\n",
" 'FOMW_hf/fsnyq', 'FOMS,hf+10log(fsnyq)'],\n",
" dtype='object')\n"
]
}
],
"source": [
"# Show headers of data table\n",
"print(df.keys())"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"# Compute aperture envelope in each year\n",
"num_avg = 2\n",
"fin_hf = np.array(df['fin_hf [Hz]'])\n",
"sndr_hf = np.array(df['SNDR_hf [dB]'])\n",
"year = np.array(df['YEAR'])\n",
"year_range = np.arange(np.min(year), np.max(year) + 1)\n",
"jitter_asymp = np.zeros(len(year_range))\n",
"\n",
"# Compute averages using dB numbers to attenuate annual outliers\n",
"for i in range(len(year_range)):\n",
" idx = np.where(year <= year_range[i])\n",
" jitter_estimate = 1/(2*np.pi*fin_hf[idx] * np.sqrt(10**(sndr_hf[idx]/10)))\n",
" smallest = sorted(jitter_estimate, key = lambda x : float('inf') if np.isnan(x) else x)[:num_avg]\n",
" jitter_asymp[i] = np.mean(smallest)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"id": "d5JmQy4ucGeD"
},
"outputs": [],
"source": [
"# Decent settings for a PowerPoint figure\n",
"font=16\n",
"size=(8, 5)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 507
},
"id": "0v3-8r5ooVn-",
"outputId": "eaec4c5b-6e89-4f25-8c3d-f64eb7380914"
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=size)\n",
"ax.plot(year_range, jitter_asymp*1e12, 'r.-', ms=15)\n",
"ax.plot(year_range, np.ones_like(year_range)*0.05, 'k--', linewidth=1, label='50 fs')\n",
"ax.legend(frameon=1, loc='lower left', framealpha=1.0, fontsize=font-3)\n",
"ax.set_xlim((year_range[0]-2, year_range[-1]+2))\n",
"ax.set_ylim((0.01, 5))\n",
"plt.yscale('log')\n",
"plt.xticks(fontsize=font)\n",
"plt.yticks(fontsize=font)\n",
"plt.xlabel('Year', fontsize=font)\n",
"plt.ylabel('Equivalent jitter [ps]', fontsize=font)\n",
"plt.title('ISSCC & VLSI 1997-%d' %max(df['YEAR']))\n",
"plt.grid(True)\n",
"fig.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"id": "F6V04XwJMSxZ",
"outputId": "61a6cb69-0515-4322-9924-3bf389a0122a"
},
"outputs": [],
"source": [
"# save as high-resolution png file\n",
"if IN_COLAB:\n",
" files.download('aperture_trend_plot.png')\n",
"else:\n",
" fig.savefig(\"aperture_trend_plot.png\", dpi=600)"
]
}
],
"metadata": {
"colab": {
"authorship_tag": "ABX9TyNFi75FryQZLjLMzbsULMTI",
"include_colab_link": true,
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: plots/energy_plot.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"<a href=\"https://colab.research.google.com/github/bmurmann/ADC-survey/blob/main/plots/energy_plot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MOkmxjrkkPJS"
},
"source": [
"# ADC Survey: Energy Plot"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "h4nZaDktMRTL"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.colors as mcolors\n",
"import pandas as pd\n",
"try:\n",
" from google.colab import files\n",
" IN_COLAB = True\n",
"except:\n",
" IN_COLAB = False"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "U6pyV95WkVGx"
},
"outputs": [],
"source": [
"# Read latest survey data and concatenate ISSCC and VLSI data\n",
"if IN_COLAB:\n",
" data = \"https://github.com/bmurmann/ADC-survey/blob/main/xls/ADCsurvey_latest.xlsx?raw=true\"\n",
"else:\n",
" data = \"../xls/ADCsurvey_latest.xlsx\"\n",
"df1 = pd.read_excel(data, sheet_name='ISSCC')\n",
"df2 = pd.read_excel(data, sheet_name='VLSI')\n",
"df = pd.concat([df1, df2])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VxF-jvN0rEBe",
"outputId": "c640a59b-20ec-4e92-8b36-04539b2e18ea"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['YEAR', 'ID', 'TYPE', 'ARCHITECTURE', 'TECHNOLOGY', 'TITLE', 'ABSTRACT',\n",
" 'AUTHORS', 'DOI', 'LINK', 'COMMENTS', 'VSUP1 [V]', 'VSUP2 [V]',\n",
" 'VSUP3 [V]', 'Csamp [pF]', 'AREA [mm^2]', 'SNDR_lf [dB]', 'fin_hf [Hz]',\n",
" 'SNDR_hf [dB]', 'SNR [dB]', 'DR [dB]', '-THD [dB]', 'SFDR [dB]',\n",
" 'SNDR_plot [dB]', 'P [W]', 'fs [Hz]', 'OSR', 'fsnyq [Hz]',\n",
" 'fcenter [Hz]', 'P/fsnyq [pJ]', 'FOMW_lf [fJ/conv-step]',\n",
" 'FOMW_hf [fJ/conv-step]', 'FOMS_lf [dB]', 'FOMS_hf [dB]',\n",
" 'FOMW_hf/fsnyq', 'FOMS,hf+10log(fsnyq)'],\n",
" dtype='object')\n"
]
}
],
"source": [
"# Show headers of data table\n",
"print(df.keys())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "snl3YeC_ge0g"
},
"outputs": [],
"source": [
"# Compute envelope for energy plot\n",
"smallest_three_p_fs = sorted(df['P/fsnyq [pJ]'])[:3]\n",
"p_fs_min = np.mean(smallest_three_p_fs)\n",
"enob = (df['SNDR_plot [dB]'] - 1.76)/6.02\n",
"k = df['P/fsnyq [pJ]'] / 4**enob\n",
"smallest_three_k = sorted(k)[:3]\n",
"k_min = np.mean(smallest_three_k)\n",
"env_x = np.linspace(10, 130, 100)\n",
"env_y = p_fs_min + k_min*4**((env_x-1.76)/6.02)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "d5JmQy4ucGeD",
"outputId": "0c0f955d-6f40-4264-c60c-91d126f330b9"
},
"outputs": [],
"source": [
"# Decent settings for a PowerPoint figure\n",
"font=16\n",
"size=(8, 5)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 507
},
"id": "0v3-8r5ooVn-",
"outputId": "94f07dce-b25b-438e-f947-39bb7c36dead"
},
"outputs": [
{
"data": {
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
gitextract_e0xcdi_y/
├── LICENSE
├── README.md
├── pdf/
│ └── README.md
├── plots/
│ ├── README.md
│ ├── aperture_plot.ipynb
│ ├── aperture_trend_plot.ipynb
│ ├── energy_plot.ipynb
│ ├── foms_plot.ipynb
│ ├── foms_plot.json
│ └── foms_trend_plot.ipynb
└── xls/
├── ADCsurvey_latest.ods
├── ADCsurvey_latest.xlsx
├── ADCsurvey_rev20240808.xlsx
├── ADCsurvey_rev20250609.xlsx
├── ADCsurvey_rev20250721.xlsx
├── ADCsurvey_rev20260314.xlsx
└── README.md
Condensed preview — 17 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (509K chars).
[
{
"path": "LICENSE",
"chars": 1500,
"preview": "BSD 3-Clause License\n\nCopyright (c) 2022, Boris Murmann\n\nRedistribution and use in source and binary forms, with or with"
},
{
"path": "README.md",
"chars": 2823,
"preview": "# ADC Performance Survey\n*Data collection from the ISSCC & VLSI Circuit Symposium, 1997-2026*\n\nFor use in publications a"
},
{
"path": "pdf/README.md",
"chars": 33,
"preview": "Various resources in PDF format.\n"
},
{
"path": "plots/README.md",
"chars": 3754,
"preview": "This directory provides some examples for plotting the ADC survey data. The Jupyter notebooks can be opened and run in G"
},
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"chars": 94426,
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"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"vie"
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"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"vie"
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"chars": 78,
"preview": "{\n \"foms_db_asymp\": 186.72331722916883,\n \"f_corner\": 46291597.96076761\n}"
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"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"vie"
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"chars": 190,
"preview": "The \"latest\" XLS file is identical to the file with the most recent date stamp. Updates typically occur each year after "
}
]
// ... and 6 more files (download for full content)
About this extraction
This page contains the full source code of the bmurmann/ADC-survey GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 17 files (492.4 KB), approximately 323.5k tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
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