analyze_error_by_phase#

Overview#

analyze_error_by_phase performs AM/PM (Amplitude Modulation / Phase Modulation) decomposition of ADC errors as a function of the input signal phase. This reveals whether errors are signal-dependent and whether they modulate the amplitude (AM) or timing/phase (PM) of the signal.

Syntax#

from adctoolbox import analyze_error_by_phase

# Basic usage with auto-detected frequency
result = analyze_error_by_phase(signal, show_plot=True)

# With specified frequency
result = analyze_error_by_phase(signal, norm_freq=0.123, n_bins=100,
                                show_plot=True)

# Exclude base noise term
result = analyze_error_by_phase(signal, include_base_noise=False,
                                show_plot=True)

Parameters#

  • signal (array_like) — Input ADC signal (sine wave excitation)

  • norm_freq (float, optional) — Normalized frequency (f/fs), range (0, 0.5)

    • If None: auto-detected via FFT

  • n_bins (int, default=100) — Number of phase bins for visualization

  • include_base_noise (bool, default=True) — Include base noise term in fitting

  • show_plot (bool, default=True) — Display error vs. phase plot

  • axes (tuple, optional) — Tuple of (ax1, ax2) for top and bottom panels

  • ax (matplotlib axis, optional) — Single axis to split into 2 panels

  • title (str, optional) — Test setup description for title

Returns#

Dictionary containing:

Numerical Results:

  • am_noise_rms_v — AM noise RMS (amplitude modulation)

  • pm_noise_rms_v — PM noise RMS in voltage units

  • pm_noise_rms_rad — PM noise RMS in radians

  • base_noise_rms_v — Base noise RMS (signal-independent)

  • total_rms_v — Total error RMS

Validation Metrics:

  • r_squared_raw — R² for raw data fit (energy ratio)

  • r_squared_binned — R² for binned data (model confidence)

Visualization Data:

  • bin_error_rms_v — RMS error per phase bin

  • bin_error_mean_v — Mean error per phase bin

  • phase_bin_centers_rad — Phase bin centers (radians)

Metadata:

  • amplitude, dc_offset, norm_freq — Fitted signal parameters

  • fitted_signal, error, phase — Signal decomposition

Algorithm#

AM/PM Model#

Error is decomposed as:

error(φ) = AM·sin(φ) + PM·cos(φ) + base_noise

where:

  • AM: Amplitude modulation error (gain variation with signal level)

  • PM: Phase modulation error (timing jitter, settling errors)

  • base_noise: Signal-independent noise

  • φ: Signal phase

Dual-Track Analysis#

Path A (Raw): Fit all N samples → highest precision AM/PM values Path B (Binned): Compute binned statistics → visualization

Cross-validation: Path A coefficients predict Path B trend → R² metric

Examples#

Example 1: AM/PM Decomposition#

import numpy as np
from adctoolbox import analyze_error_by_phase

# Analyze ADC signal
result = analyze_error_by_phase(adc_signal, show_plot=True)

print(f"AM noise: {result['am_noise_rms_v']*1e6:.2f} µV RMS")
print(f"PM noise: {result['pm_noise_rms_rad']*1e12:.2f} pico-radians RMS")
print(f"Base noise: {result['base_noise_rms_v']*1e6:.2f} µV RMS")
print(f"Total RMS: {result['total_rms_v']*1e6:.2f} µV RMS")
print(f"R² (model fit): {result['r_squared_raw']:.4f}")

Example 2: Identify Dominant Error Mechanism#

result = analyze_error_by_phase(signal, show_plot=False)

am = result['am_noise_rms_v']
pm = result['pm_noise_rms_v']
base = result['base_noise_rms_v']

# Determine dominant error source
errors = {'AM': am, 'PM': pm, 'Base': base}
dominant = max(errors, key=errors.get)

print(f"Dominant error: {dominant}")
print(f"  AM:   {am*1e6:.2f} µV ({am/result['total_rms_v']*100:.1f}%)")
print(f"  PM:   {pm*1e6:.2f} µV ({pm/result['total_rms_v']*100:.1f}%)")
print(f"  Base: {base*1e6:.2f} µV ({base/result['total_rms_v']*100:.1f}%)")

Example 3: Compare Multiple Conditions#

import matplotlib.pyplot as plt

conditions = {
    'Ideal': signal_ideal,
    'With Jitter': signal_jitter,
    'With Gain Error': signal_gain_error,
}

fig, axes = plt.subplots(len(conditions), 2, figsize=(12, 4*len(conditions)))

for i, (name, sig) in enumerate(conditions.items()):
    result = analyze_error_by_phase(sig, axes=axes[i], title=name,
                                     show_plot=True)
    print(f"{name}: AM={result['am_noise_rms_v']*1e6:.1f}µV, "
          f"PM={result['pm_noise_rms_rad']*1e12:.1f}pRad")

plt.tight_layout()
plt.show()

Interpretation#

Error Type Classification#

Dominant Component

Likely Cause

AM dominant

Gain error, amplitude-dependent distortion, residue amplifier gain variation

PM dominant

Clock jitter, timing errors, settling time issues

Base noise dominant

Thermal noise, quantization noise (signal-independent)

AM ≈ PM

Mixed analog impairments

Phase Pattern Analysis#

Error vs. Phase Pattern

Interpretation

Sinusoidal (AM-like)

Amplitude-dependent error

Cosinusoidal (PM-like)

Timing/phase-dependent error

Flat (uniform)

Signal-independent noise

Complex shape

Multiple error mechanisms

R² Interpretation#

  • R² > 0.9: Model fits well, errors are signal-dependent

  • 0.5 < R² < 0.9: Moderate signal dependence

  • R² < 0.5: Errors mostly signal-independent (random noise)

Use Cases#

  • Distinguish jitter from amplitude errors

  • Identify memory effects in pipelined ADCs

  • Validate settling time in SAR ADCs

  • Characterize residue amplifier gain variations

  • Debug clock quality (PM noise indicates jitter)

Common Patterns#

Pipelined ADC#

  • High AM → Residue amplifier gain error

  • High PM → Inadequate settling time

SAR ADC#

  • High AM → DAC mismatch, reference variation

  • High PM → Comparator metastability

Flash ADC#

  • Low AM, low PM → Good performance

  • High base noise → Comparator noise

See Also#

References#

  1. IEEE Std 1241-2010, “IEEE Standard for Terminology and Test Methods for ADCs”

  2. M. Soudan et al., “A Novel AM-PM-Jitter Decomposition Method for Characterizing ADC Nonidealities,” IEEE Trans. IM, 2008