Noise Analysis

Noise Analysis#

Perform stochastic noise analysis at the DC operating point.

import os
import sys
from pathlib import Path

# Set working directory to the PDK root if running from scripts/
if Path.cwd().name == "scripts":
    os.chdir(Path.cwd().parent)

# Ensure ngspice shared library can be found (macOS homebrew)
if sys.platform == "darwin" and "/opt/homebrew/lib" not in os.environ.get(
    "DYLD_LIBRARY_PATH", ""
):
    os.environ["DYLD_LIBRARY_PATH"] = "/opt/homebrew/lib:" + os.environ.get(
        "DYLD_LIBRARY_PATH", ""
    )

import numpy as np
import pandas as pd
import holoviews as hv
from nyancad.watch import watch_project_dir, file_schematic
from nyancad.netlist import inspice_netlist
from InSpice import Simulator

hv.extension("bokeh")
ⓘ

Parameters#

SCHEMATIC = "rcfilter"  # stem of the .nyancir file
CORNER = "mos_tt"

# -- Noise parameters --
OUTPUT_NODE = "P2"  # output node
REF_NODE = "GND"  # reference node
INPUT_SOURCE = "VV1"  # input source name
VARIATION = "dec"  # point spacing: "dec", "oct", or "lin"
NUM_POINTS = 10  # number of points
START_FREQ = 10  # start frequency (Hz)
STOP_FREQ = 1e5  # stop frequency (Hz)
POINTS_PER_SUMMARY = 0  # points per summary (0 for no summary)

Load schematic and build netlist#

project = watch_project_dir(".")
schem_data = await file_schematic(project, SCHEMATIC)
spice = await inspice_netlist(SCHEMATIC, schem_data, corner=CORNER)
print(spice)

.title schematic
VV1 net0 GND DC 0 AC 1
CC1 P2 net0 1u
RR1 P2 GND 1k

print("Netlist nodes:", list(spice.node_names))
print("Netlist elements:", list(spice.element_names))

Netlist nodes: ['0', 'net0', 'GND', 'P2']
Netlist elements: ['VV1', 'CC1', 'RR1']

Run noise analysis#

simulator = Simulator.factory(simulator="ngspice-shared")

for osdi in [
    "ihp/models/ngspice/osdi/psp103.osdi",
    "ihp/models/ngspice/osdi/psp103_nqs.osdi",
    "ihp/models/ngspice/osdi/r3_cmc.osdi",
    "ihp/models/ngspice/osdi/mosvar.osdi",
]:
    simulator._ngspice_shared.exec_command(f"osdi {osdi}")

simulation = simulator.simulation(spice)
simulation.noise(
    output_node=OUTPUT_NODE,
    ref_node=REF_NODE,
    src=INPUT_SOURCE,
    variation=VARIATION,
    points=NUM_POINTS,
    start_frequency=START_FREQ,
    stop_frequency=STOP_FREQ,
    points_per_summary=POINTS_PER_SUMMARY if POINTS_PER_SUMMARY > 0 else None,
)
print("Noise analysis complete.")

# ngspice 46 populates two plots: noise1 (spectrum vs frequency) and noise2
# (integrated totals). InSpice's shared-lib plot() can't read noise1's vectors
# directly on ngspice 46, so use ngspice's wrdata to dump them to a file.
import os as _os
import tempfile

_out = tempfile.NamedTemporaryFile(suffix=".data", delete=False).name
simulator._ngspice_shared.exec_command(
    f"wrdata {_out} noise1.frequency noise1.inoise_spectrum noise1.onoise_spectrum"
)
_raw = np.loadtxt(_out)
_os.unlink(_out)
freq = _raw[:, 0]
inoise_spectrum = _raw[:, 3]
onoise_spectrum = _raw[:, 5]
totals = simulator._ngspice_shared.plot(simulation, "noise2").to_analysis()
print(f"Integrated input-referred noise:  {float(np.array(totals.inoise_total)):.3e}")
print(f"Integrated output-referred noise: {float(np.array(totals.onoise_total)):.3e}")

Warning: can't find the initialization file spinit.

Newer Ngspice version that could be unsupported 46

Noise analysis complete.
Integrated input-referred noise:  1.847e-07
Integrated output-referred noise: 6.297e-08

/var/folders/f6/0bfsmxqs4dd6486gr3438tnw0000gn/T/ipykernel_11336/3717702115.py:38: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  print(f"Integrated input-referred noise:  {float(np.array(totals.inoise_total)):.3e}")
/var/folders/f6/0bfsmxqs4dd6486gr3438tnw0000gn/T/ipykernel_11336/3717702115.py:39: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  print(f"Integrated output-referred noise: {float(np.array(totals.onoise_total)):.3e}")

Results#

df = pd.DataFrame(
    {"inoise_spectrum": inoise_spectrum, "onoise_spectrum": onoise_spectrum},
    index=pd.Index(freq, name="frequency"),
)
df
inoise_spectrum onoise_spectrum
frequency
10.000000 6.479789e-08 4.063359e-09
12.589254 5.147079e-08 4.058694e-09
15.848932 4.088470e-08 4.051333e-09
19.952623 3.247588e-08 4.039750e-09
25.118864 2.579650e-08 4.021592e-09
31.622777 2.049089e-08 3.993310e-09
39.810717 1.627649e-08 3.949682e-09
50.118723 1.292888e-08 3.883374e-09
63.095734 1.026977e-08 3.784799e-09
79.432823 8.157571e-09 3.642869e-09
100.000000 6.479789e-09 3.447365e-09
125.892541 5.147079e-09 3.193168e-09
158.489319 4.088470e-09 2.884921e-09
199.526231 3.247588e-09 2.538830e-09
251.188643 2.579650e-09 2.179068e-09
316.227766 2.049089e-09 1.830344e-09
398.107171 1.627649e-09 1.511350e-09
501.187234 1.292888e-09 1.232249e-09
630.957344 1.026977e-09 9.957865e-10
794.328235 8.157571e-10 7.998596e-10
1000.000000 6.479789e-10 6.399248e-10
1258.925410 5.147079e-10 5.106435e-10
1584.893190 4.088470e-10 4.068011e-10
1995.262310 3.247588e-10 3.237305e-10
2511.886430 2.579650e-10 2.574488e-10
3162.277660 2.049089e-10 2.046499e-10
3981.071710 1.627649e-10 1.626350e-10
5011.872340 1.292888e-10 1.292236e-10
6309.573440 1.026977e-10 1.026651e-10
7943.282350 8.157571e-11 8.155934e-11
10000.000000 6.479789e-11 6.478969e-11
12589.254100 5.147079e-11 5.146668e-11
15848.931900 4.088470e-11 4.088264e-11
19952.623100 3.247588e-11 3.247484e-11
25118.864300 2.579650e-11 2.579599e-11
31622.776600 2.049089e-11 2.049063e-11
39810.717100 1.627649e-11 1.627636e-11
50118.723400 1.292888e-11 1.292881e-11
63095.734400 1.026977e-11 1.026974e-11
79432.823500 8.157571e-12 8.157555e-12
100000.000000 6.479789e-12 6.479781e-12 
plot = hv.NdOverlay(
    {
        k: hv.Curve((df.index, df[k]), "Frequency (Hz)", "Noise spectral density").opts(
            logx=True, logy=True
        )
        for k in df.columns
    }
)
plot.opts(responsive=True, height=500)