fabtwin

Learned generative process twins for yield-aware inverse design of multilayer optics. Real deposition errors are systematic, layer-correlated, design-conditional, skewed and heavy-tailed, and manufacturing yield is decided precisely in the tail that hand-specified Gaussian tolerance models represent worst. fabtwin is the generalized library form of the FabGAN-ID framework (Mahim, Islam, Rahman and Mohsin, IEEE Sensors Journal, 2026): learn the fabrication process from historical (recipe, outcome) traces, then robustify designs by ascending the yield-deciding lower-tail statistic pathwise through the learned twin and the exact physics at once.

Learn what cannot be simulated; differentiate what can.

Where the paper's companion repository reproduces the paper (fixed 20-layer SiNx notch platform, JAX required end to end), fabtwin is the reusable instrument: any layer count, any design box, any wavelength grid, any cited material system, any linear-in-T merit -- and the entire differentiable fabrication loop runs on NumPy alone, because the discrete adjoint of the transfer-matrix recursion is derived by hand rather than delegated to an autodiff framework. The learned conditional WGAN-GP twin is an optional extra (pip install 'fabtwin[twin]', JAX + optax), and its autodiff gradients are asserted equal to the hand adjoint to machine precision -- two independent derivations of the same physics agreeing is the package's core cross-validation.

What is inside

• fabtwin.tmm -- exact characteristic-matrix optics (Macleod) for arbitrary stratified stacks: normal or oblique incidence (s/p), absorbing layers (n + ik, gain refused), reflectance/transmittance, and linear-in-T merit builders (fluorescence-rejection notch and bandpass, per the paper's sensor front-ends). Cross-validated against the open tmm reference (Byrnes, arXiv:1603.02720) to 1e-12 and against closed forms: bare-interface Fresnel, the exact absentee half-wave layer, the exact quarter-wave antireflection transform, the analytic Brewster zero, and R + T = 1 at machine precision.
• fabtwin.adjoint -- the hand-derived exact adjoint: merit_and_grad returns dJ/d(thickness) and dJ/d(index) from two O(NL) sweeps of prefix/suffix products. Matches central finite differences at the paper's own accuracy figure and vanishes exactly at the closed-form quarter-wave optimum.
• fabtwin.materials -- a cited Sellmeier engine (Si3N4: Luke 2015; fused silica: Malitson 1965; both CC0 via refractiveindex.info) with validity-range refusal instead of silent extrapolation, and the variable-index layer construction of the single-material SiNx platform (Yesilyurt 2023).
• fabtwin.process -- the six-mechanism virtual deposition process of the FabGAN-ID benchmark as a parametric dataclass (systematic bias, index drift, intermixing, design-conditional AR(1) thickness noise, right-skewed index noise, rare particulates): every mechanism re-scalable or removable, the paper's published magnitudes as cited defaults, and an exact closed-form anchor when the randomness is off.
• fabtwin.twins -- the common error parameterization x = (t̃/t − 1, ñ − n) with exact round trip, and Gaussian twins (diagonal/full) as differentiable parametric baselines.
• fabtwin.risk -- CVaR (exact sorted-tail convention, Rockafellar & Uryasev 2000), tail statistics, the hard pass/fail filter yield, and Monte-Carlo scoring of a design under any process or twin.
• fabtwin.design -- the probe-seeded projected-Adam adjoint engine and its equal-budget random-search baseline; recovers the analytic quarter-wave optimum in the tests.
• fabtwin.robust -- pathwise CVaR (or mean − beta*sigma) robustification in pure NumPy for affinely reparameterized twins, with the frozen-latent gradient anchored against finite differences.
• fabtwin.twin_jax (the [twin] extra) -- the conditional, moment-matched WGAN-GP process twin, optional physics-in-the-loop tail calibration (the paper's honest finding is reproduced in the docstring: it helps only when traces are scarce), a JAX solver asserted equal to the NumPy one, and robustify_gan: the paper's Stage 2, CVaR ascent through generator and solver jointly.

Install

pip install fabtwin              # NumPy core: physics, adjoint, Gaussian twins, CVaR loop
pip install 'fabtwin[twin]'      # + JAX/optax: the learned WGAN-GP twin

The loop in five lines

import numpy as np, fabtwin as ft

lam = np.linspace(0.40, 0.80, 161)                     # um
S = ft.dispersion_shape(lam, 0.550)                    # SiNx shape (Luke 2015)
nsub = ft.SIO2_MALITSON1965.n(lam)                     # substrate (Malitson 1965)
w, c0 = ft.notch_weights(lam, 0.532, 0.015, 0.030)     # 532-nm rejection merit
box = ft.DesignBox(20, 0.020, 0.120, 1.6, 2.4)         # fabrication box

t, n0, J, _ = ft.inverse_design(lam, S, w, c0, box, n_sub=nsub)   # nominal optimum

rng = np.random.default_rng(0)
recipes = box.sample(rng, 200)
rt, rn, ftd, fnd = ft.PAPER_PROCESS.trace_dataset(*recipes, 2, rng)  # historical traces
twin = ft.GaussianTwin(ft.errors_from_traces(rt, rn, ftd, fnd))      # or train the WGAN twin

t_rob, n_rob, hist = ft.robustify(lam, t, n0, S, w, c0, twin, box,
                                  alpha=0.05, n_sub=nsub)            # CVaR ascent
report = ft.evaluate_under_process(
    lambda a, b, K: ft.PAPER_PROCESS.ensemble(a, b, K, rng),
    t_rob, n_rob, lam, S, w, c0, K=2000, n_sub=nsub)                 # score vs the process

The learned twin drops in through the [twin] extra: fabtwin.twin_jax.train_wgan on the same traces, then robustify_gan ascends the identical objective through the generator.

Status

v0.1.0 (alpha). Implemented and tested (37 tests, Python 3.10-3.13; the JAX extra's tests skip cleanly without it): everything listed above, with every physics claim anchored to a closed form, an independent reference implementation, or an exact identity -- never to a stored number. The API may change before v1.0.

Deliberate scope, designed out with reasons rather than overlooked:

• The hand adjoint covers normal incidence and lossless (real-index) designs -- the regime of the FabGAN-ID loop. The oblique and absorbing forward solver is provided; its gradients are not faked.
• No specification-conditioned correction policies (the paper's Stage 3): the paper itself scopes that study as exploratory, and a policy layer would import its chain-GCN encoder wholesale rather than generalize it. The pathwise machinery a policy trainer needs is all here.
• No neural forward surrogates, by result: the paper's protocol study found the exact differentiable solver strictly dominates them in this regime (faster, exact, with exact gradients), so shipping one would package a documented mistake.
• The benchmark data (300 designs / 48,300 samples / 400 traces) stays with the paper's companion repository and its Zenodo archive; this package regenerates equivalent data from DepositionProcess instead of redistributing files.

Associated paper

T. M. Mahim, M. N. Islam, M. M. Rahman, A. S. M. Mohsin, "FabGAN-ID: Learning the Fabrication Process for Yield-Aware Inverse Design of Multilayer Photonic Sensor Filters", IEEE Sensors Journal (2026). Companion repository: Learned-generative-process-twins... (benchmark archived on Zenodo, doi:10.5281/zenodo.21315793).

Support and governance

The package is written and maintained by Tanvir Mahmud Mahim (Department of Electrical and Electronic Engineering, BRAC University), who reviews every change and takes the final decision on scope and releases. There is no separate governance body; design questions are discussed in the open in issues and pull requests, and the standing rule of CONTRIBUTING.md binds the maintainer exactly as it binds contributors: a change that touches physics arrives with a test, and a constant arrives with its source.

License and citation

Apache-2.0. Cite via CITATION.cff and the associated paper above.