Skip to main content

Wafer-level Zernike polynomial decomposition and fitting

Project description

wlzpoly — Wafer-Level Zernike Polynomials

Decompose 13-point wafer thickness measurements into 9 Zernike coefficients (LSQ / Ridge), with a reproducible demo workflow that generates synthetic data, fits it, and verifies the recovered coefficients against ground truth.

Install

pip install wlzpoly

Requires Python 3.9+. Dependencies: numpy >= 1.22, pandas >= 1.5, matplotlib >= 3.5, tqdm >= 4.60.

Quick start

import numpy as np
from wlzpoly import ZernikePolynomials, WaferLevelZernikePolynomials

# 1) Build a wavefront from known coefficients (Noll j -> a_j)
z = ZernikePolynomials(coeffs={1: 500.0, 4: -12.0, 6: 0.5}, n_terms=9)
field = z.evaluate(rho=np.array([0.0, 0.5, 1.0]),
                   theta=np.array([0.0, 0.0, 0.0]))

# 2) Fit Zernike coefficients from measurements at known coordinates
#    coords_df   : DataFrame indexed by point_id, columns ['x','y'] (mm),
#                  attrs['wafer_radius_mm']
#    df_measured : DataFrame indexed by MultiIndex(wafer_id, point_id),
#                  column ['T']
wlz = WaferLevelZernikePolynomials(
    coords_df=coords_df, coordinate="cartesian", n_terms=9,
)
fit_results = wlz.fit_coefficients(mesured_df=df_measured, solver="lsq")
# fit_results : list of {"id": <wafer_id>, "coeffs": np.ndarray}

# 3) Render a fitted wafer field
fig = wlz.draw_field(coeffs=fit_results[0]["coeffs"])
fig.savefig("W_01_fit.png", dpi=130, bbox_inches="tight")

ZernikePolynomials follows the Noll convention and supports any radial order — the j -> (n, m) mapping is computed dynamically.

Public API

from wlzpoly import (
    ZernikePolynomials,           # pure-math + per-wavefront instance
    WaferLevelZernikePolynomials, # wafer-aware (coords + measurements -> fit)
    fit_lsq, fit_ridge,           # general-purpose linear solvers
)
Function / class Purpose
ZernikePolynomials.basis(j, rho, theta) Single Zernike basis Z_j(rho, theta)
ZernikePolynomials.basis_matrix(rho, theta, n_terms=...) Design matrix A for fitting
ZernikePolynomials.pyramid_image(n_max=..., names=..., return_type=...) Zernike pyramid PNG / Figure
ZernikePolynomials(coeffs=...).evaluate(rho, theta) Evaluate a specific wavefront
WaferLevelZernikePolynomials(coords_df, coordinate, n_terms) Pre-compute A from measurement layout
wlz.fit_coefficients(mesured_df, solver, lam) Per-wafer LSQ / Ridge fit
wlz.draw_field(coeffs) Heatmap with measurement-point overlay
fit_lsq(A, T) a_hat = (A^T A)^-1 A^T T
fit_ridge(A, T, lam) a_hat = (A^T A + lam I)^-1 A^T T
loocv_lambda(A, T, lambdas) LOOCV-driven lambda selection
wlzpoly.decompose.load_wafer_coordinates(wafer_points_file, coordinate) Read points JSON into a DataFrame
wlzpoly.decompose.load_measured_data(target_file) Read target CSV into long-format DataFrame

Three-stage demo (after development install)

git clone https://github.com/ykim2718/WaferLevelZernikePolynomials.git
cd WaferLevelZernikePolynomials
pip install -e .

cd examples
.\run_demo.ps1                                   # runs all three stages

run_demo.ps1 invokes the three stages in order:

python generate_samples.py ...                   # Stage 1: synthesize wafers
                                                 #   -> samples/target_file.csv,
                                                 #      samples/ground_truth.csv, ...
python -m wlzpoly.decompose --target_file ...    # Stage 2: fit Zernike coefficients
python -m wlzpoly.verify --target_file ...       # Stage 3: compare vs ground truth
                       --ground_truth_file ...

Every parameter is a CLI flag — --working_folder, --wafer_points, --target_file, --ground_truth_file, --n_terms, --solver, etc. See python -m wlzpoly.decompose -h / verify -h for the full list. Demo outputs land in examples/{samples,decomposition,verification}/; pre-generated copies are visible on the GitHub repo.

Documentation

Full documentation — folder layout, CLI options, configuration files, output schemas, scenario reference, recipes, algorithm summary — lives on the GitHub README:

https://github.com/ykim2718/WaferLevelZernikePolynomials

Links

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

wlzpoly-0.0.1.tar.gz (27.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

wlzpoly-0.0.1-py3-none-any.whl (24.1 kB view details)

Uploaded Python 3

File details

Details for the file wlzpoly-0.0.1.tar.gz.

File metadata

  • Download URL: wlzpoly-0.0.1.tar.gz
  • Upload date:
  • Size: 27.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.14

File hashes

Hashes for wlzpoly-0.0.1.tar.gz
Algorithm Hash digest
SHA256 a057abf95e26e9cd51712890b82179f68f2214be68e510d073c660fec435a609
MD5 6fdbba9758ab327838059a2574f3353f
BLAKE2b-256 1aed835f0c0f50ba5aa5898e2fc8899cf58ef4f14e96ffd359c4a39382770c3d

See more details on using hashes here.

File details

Details for the file wlzpoly-0.0.1-py3-none-any.whl.

File metadata

  • Download URL: wlzpoly-0.0.1-py3-none-any.whl
  • Upload date:
  • Size: 24.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.14

File hashes

Hashes for wlzpoly-0.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 7225ec68e30032fc59f49e32f09a489e4af2d3d81700d4166f48937a6c4c60d7
MD5 fdb46fdb67ce8ebd13442888a884b0d5
BLAKE2b-256 f1aea1f4e5b4881ebb281e322a1d69b276320be5da04517922b232b33fa9ca4a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page