A Comprehensive Toolbox for ADC Characterization, Calibration, and Visualization.
Unlocking ADC Insights through Multi-Dimensional Analysis.
-
Comprehensive Spectrum Analysis
- Full Metric Suite: Extraction of ENOB, SNDR, SNR, SFDR, THD, NSD, and Noise Floor.
- Smart labeling: Automated labeling for harmonics, noise floor, and OSR bandwidth.
- Polar Spectrum: Visualizes phase errors to distinguish static from dynamic nonlinearities.
- Two-Tone Spectrum: Supports dual-tone testing with IMD calculation.
- Validated Signal Processing: Eight window functions; two averaging modes (power spectrum averaging & complex/coherent spectrum averaging)
-
Advanced Error Visualization
- goes beyond standard plots with Polar Spectrum Analysis to visualize
- phase/magnitude relationships and Error Envelope plots to identify dynamic nonlinearities.
- decomposes ADC errors into time-domain (INL/DNL), frequency-domain (Harmonics/Spurs), and statistical components (PDF/Histograms) for root-cause analysis.
-
Realistic Signal Modeling: generates high-fidelity ADC waveforms simulating real-world impairments, including clock jitter, thermal noise, quantization effects, and settling errors.
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Calibration Algorithms: built-in reference implementations for digital calibration, including bit-weight extraction and redundancy management for SAR and Pipeline architectures.
# Install
pip install adctoolbox
# Upgrade if already installed
pip install --upgrade adctoolbox
# Check version
python -c "import adctoolbox; print(adctoolbox.__version__)"Get all 45 examples in one command:
cd /path/to/your/workspace
adctoolbox-get-examplesThis creates an adctoolbox_examples/ directory with all examples organized by category.
Run an example:
cd adctoolbox_examples/02_spectrum
python exp_s01_analyze_spectrum_simplest.pyUse in your code:
from adctoolbox import analyze_spectrum
# Analyze signal spectrum
result = analyze_spectrum(signal, fs=800e6, show_plot=True)
print(f"ENOB: {result['enob']:.2f} bits, SNDR: {result['sndr_db']:.2f} dB")See Usage Examples section below for detailed code examples.
Environment verification and coherent sampling basics.
| Example | Description |
|---|---|
exp_b01_environment_check.py |
Verify installation and plot test signal |
exp_b02_coherent_vs_non_coherent.py |
Demonstrate coherent vs non-coherent sampling impact on ENOB |
Spectrum analysis with windowing, averaging, polar plots, and two-tone testing.
| Example | Description |
|---|---|
exp_s01_analyze_spectrum_simplest.py |
Simplest spectrum analysis example |
exp_s02_analyze_spectrum_interactive.py |
Interactive spectrum visualization |
exp_s03_analyze_spectrum_savefig.py |
Save spectrum plots with amplitude comparison |
exp_s04_sweep_dynamic_range.py |
Dynamic range measurement (SNR vs amplitude) |
exp_s05_annotating_spur.py |
Annotate and identify spurs in spectrum |
exp_s06_sweeping_fft_and_osr.py |
FFT length and OSR comparison (resolution vs SNR) |
exp_s07_spectrum_averaging.py |
Coherent averaging demonstration |
exp_s08_windowing_deep_dive.py |
Window function comparison (8 windows × 3 scenarios) |
exp_s10_polar_noise_and_harmonics.py |
Polar phase spectrum: noise vs harmonics |
exp_s11_polar_memory_effect.py |
Memory effect analysis via polar spectrum |
exp_s12_polar_coherent_averaging.py |
Coherent averaging with polar plots |
exp_s21_analyze_two_tone_spectrum.py |
Two-tone spectrum analysis (IMD2/IMD3) |
exp_s22_two_tone_imd_comparison.py |
IMD product comparison across frequencies |
exp_s23_two_tone_spectrum_averaging.py |
Power vs coherent averaging for two-tone |
Generate ADC signals with various impairments for testing and validation.
| Example | Description |
|---|---|
exp_g01_generate_signal_demo.py |
Thermal noise demonstration (4 noise levels) |
exp_g03_sweep_quant_bits.py |
Quantization noise vs bit resolution (2-16 bits) |
exp_g04_sweep_jitter_fin.py |
Jitter vs input frequency sweep |
exp_g05_sweep_static_nonlin.py |
Static nonlinearity (HD2/HD3 sign combinations) |
exp_g06_sweep_dynamic_nonlin.py |
Dynamic effects (settling, memory, RA gain) |
exp_g07_sweep_interferences.py |
Interference types (glitch, AM, clipping, drift) |
Time-domain, frequency-domain, and statistical error analysis on analog waveforms.
| Example | Description |
|---|---|
exp_a01_fit_sine_4param.py |
4-parameter sine fitting (DC, amplitude, frequency, phase) |
exp_a02_analyze_error_by_value.py |
Error histograms binned by ADC code |
exp_a03_analyze_error_by_phase.py |
Decompose error into AM/PM noise components |
exp_a04_jitter_calculation.py |
Jitter measurement and validation |
exp_a11_decompose_harmonics.py |
Time-domain harmonic decomposition |
exp_a12_decompose_harmonics_polar.py |
Polar plot harmonic decomposition |
exp_a21_analyze_error_pdf.py |
Error probability distribution (15 non-idealities) |
exp_a22_analyze_error_spectrum.py |
Error spectrum analysis |
exp_a23_analyze_error_autocorrelation.py |
Temporal correlation in error signal |
exp_a24_analyze_error_envelope_spectrum.py |
Error envelope spectrum (AM patterns) |
exp_a25_spectra.py |
Spectrum comparison across non-idealities |
exp_a31_fit_static_nonlin.py |
Extract k2/k3 nonlinearity coefficients |
exp_a32_inl_from_sine_sweep_length.py |
INL/DNL vs record length (N = 2^10 to 2^16) |
Digital code analysis for pipeline and SAR ADCs with calibration algorithms.
| Example | Description |
|---|---|
exp_d01_bit_activity.py |
Bit flip activity visualization |
exp_d02_cal_weight_sine.py |
Foreground weight calibration using sine wave |
exp_d03_redundancy_comparison.py |
Architecture comparison (1.5-bit vs 2-bit stages) |
exp_d04_weight_scaling.py |
Digital weight scaling analysis |
exp_d05_sweep_bit_enob.py |
ENOB vs bit resolution sweep |
Helper functions for unit conversions and metric calculations.
| Example | Description |
|---|---|
exp_b01_aliasing_nyquist_zones.py |
Aliasing and Nyquist zone demonstration |
exp_b02_unit_conversions.py |
dB, magnitude, SNR, ENOB conversions |
exp_b03_calculate_fom.py |
Figure of Merit (FoM) calculations |
exp_b05_amplitudes_to_snr.py |
Amplitude to SNR conversion |
exp_b06_convert_nsd_snr.py |
Noise Spectral Density and SNR conversion |
Spectrum Analysis
from adctoolbox import analyze_spectrum, analyze_two_tone_spectrum
# Single-tone analysis
result = analyze_spectrum(signal, fs=800e6, harmonic=5, show_plot=True)
print(f"ENOB: {result['enob']:.2f} bits, SNDR: {result['sndr_db']:.2f} dB")
# Two-tone analysis (IMD)
result = analyze_two_tone_spectrum(signal, fs=1000e6, show_plot=True)
print(f"IMD2: {result['imd2_db']:.2f} dB, IMD3: {result['imd3_db']:.2f} dB")Error Analysis (Auto-fits sine internally)
from adctoolbox import (
analyze_error_pdf,
analyze_error_spectrum,
analyze_error_autocorr,
analyze_error_envelope_spectrum
)
# Error PDF
result = analyze_error_pdf(signal, resolution=12, show_plot=True)
print(f"Std: {result['sigma']:.2f} LSB, KL div: {result['kl_divergence']:.4f}")
# Error autocorrelation
result = analyze_error_autocorr(signal, max_lag=100, show_plot=True)
# Error spectrum
result = analyze_error_spectrum(signal, fs=800e6, show_plot=True)
# Error envelope spectrum (AM detection)
result = analyze_error_envelope_spectrum(signal, fs=800e6, show_plot=True)Sine Fitting & Harmonic Decomposition
from adctoolbox import fit_sine_4param, analyze_decomposition_time
# 4-parameter sine fitting
result = fit_sine_4param(signal, frequency_estimate=0.1)
print(f"Freq: {result['frequency']:.6f}, Amp: {result['amplitude']:.4f}")
# Harmonic decomposition
result = analyze_decomposition_time(signal, fs=800e6, harmonic=5, show_plot=True)INL/DNL Extraction
from adctoolbox import analyze_inl_from_sine
result = analyze_inl_from_sine(signal, resolution=12, show_plot=True)
print(f"INL: [{result['inl'].min():.2f}, {result['inl'].max():.2f}] LSB")
print(f"DNL: [{result['dnl'].min():.2f}, {result['dnl'].max():.2f}] LSB")Digital Calibration
from adctoolbox import calibrate_weight_sine, calibrate_weight_two_tone
# Weight calibration
result = calibrate_weight_sine(digital_codes, order=5)
print(f"SNR: {result['snr_db']:.2f} dB, THD: {result['thd_db']:.2f} dB")
# Two-tone calibration
result = calibrate_weight_two_tone(digital_codes, order=5)Complete Example with Signal Generation
import numpy as np
from adctoolbox import analyze_spectrum, find_coherent_frequency, amplitudes_to_snr
# Setup
N, Fs, Fin_target = 2**13, 800e6, 80e6
Fin, Fin_bin = find_coherent_frequency(Fs, Fin_target, N)
# Generate signal
t = np.arange(N) / Fs
A, DC, noise_rms = 0.49, 0.5, 100e-6
signal = A * np.sin(2*np.pi*Fin*t) + DC + np.random.randn(N) * noise_rms
# Analyze
result = analyze_spectrum(signal, fs=Fs, harmonic=5, show_plot=True)
snr_theory = amplitudes_to_snr(sig_amplitude=A, noise_amplitude=noise_rms)
print(f"Measured SNR: {result['snr_db']:.2f} dB")
print(f"Theoretical SNR: {snr_theory:.2f} dB")- Python >= 3.10
- NumPy >= 1.23.0
- Matplotlib >= 3.6.0
- SciPy >= 1.9.0
If you use this toolbox in your research, please cite:
@software{adctoolbox2025,
author = {Zhang, Zhishuai and Lu, Jie},
title = {ADCToolbox: Comprehensive ADC Characterization and Analysis Toolkit},
year = {2025},
url = {https://github.com/Arcadia-1/ADCToolbox}
}- Zhishuai Zhang
- Lu Jie
MIT License
📚 Full Documentation - Complete API reference, algorithm guides, and tutorials
- Installation Guide - Getting started
- Quick Start - First steps with examples
- Algorithm Reference - 15 detailed algorithm guides
- API Documentation - Function signatures and parameters
- Changelog - Version history