diff --git a/mkdocs.yml b/mkdocs.yml
index e14f553c..0363a469 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -21,6 +21,7 @@ nav:
   - Directional Coupler: nbs/meep_dc.md
   - Crossing: nbs/meep_crossing.md
   - 2D FDTD: nbs/meep_2d.md
+  - 2D varEIM (Y-Branch): nbs/meep_ybranch_veim.md
   - 2D Grating Coupler: nbs/meep_2d_xz_gc.md
 - FEM for RF:
   - CPW (Lumped Ports): nbs/palace_cpw_lumped.md
diff --git a/nbs/meep_ybranch_veim.ipynb b/nbs/meep_ybranch_veim.ipynb
new file mode 100644
index 00000000..53650840
--- /dev/null
+++ b/nbs/meep_ybranch_veim.ipynb
@@ -0,0 +1,2234 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bb4740a1",
+   "metadata": {},
+   "source": [
+    "# MEEP Y-branch: 3D and 2D variational EIM\n",
+    "\n",
+    "[MEEP](https://meep.readthedocs.io/) is an open-source FDTD electromagnetic\n",
+    "simulator. This notebook reproduces the 3D S-parameter simulation of the\n",
+    "photonic Y-branch from the [MEEP example](./meep_ybranch.py), then repeats it\n",
+    "as a fast **2D variational effective-index (varEIM)** simulation and compares\n",
+    "the two.\n",
+    "\n",
+    "**Requirements:**\n",
+    "\n",
+    "- UBC PDK: `uv pip install ubcpdk`\n",
+    "- [GDSFactory+](https://gdsfactory.com) account for cloud simulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e570be48",
+   "metadata": {},
+   "source": [
+    "### Load a pcell from UBC PDK"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3226d60e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from ubcpdk import PDK, cells\n",
+    "\n",
+    "PDK.activate()\n",
+    "\n",
+    "c = cells.ebeam_y_1550()\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e277ab5a",
+   "metadata": {},
+   "source": [
+    "### Configure and run the 3D simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "feb125d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stack validation: PASSED\n",
+      "Warnings:\n",
+      "  - Stopping: energy_decay (dt=20.0, decay_by=0.01, cap=2000.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from gsim import meep\n",
+    "from gsim.common.stack import get_stack\n",
+    "from gsim.meep.models.api import Material\n",
+    "\n",
+    "stack = get_stack()  # auto-detects active PDK\n",
+    "\n",
+    "sim = meep.Simulation()\n",
+    "\n",
+    "sim.geometry(component=c, stack=stack, z_crop=\"auto\")\n",
+    "sim.materials = {\n",
+    "    \"si\": Material(refractive_index=3.47),\n",
+    "    \"SiO2\": Material(refractive_index=1.44),\n",
+    "}\n",
+    "sim.source(port=\"o1\", wavelength=1.55, wavelength_span=0.01)\n",
+    "sim.monitors = [\"o1\", \"o2\", \"o3\"]\n",
+    "sim.domain(pml=1.0, margin=0.5)\n",
+    "sim.solver(resolution=20, simplify_tol=0.01, save_animation=True, verbose_interval=5.0)\n",
+    "sim.solver.stop_when_energy_decayed()\n",
+    "\n",
+    "print(sim.validate_config())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "14530eb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sim.plot_2d(slices=\"xyz\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8963ede",
+   "metadata": {},
+   "source": [
+    "### Run 3D simulation on cloud"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5bdc5cae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  meep-d7104144  completed  3m 34s\n",
+      "Extracting results.tar.gz...\n",
+      "Downloaded 247 files to /Users/nath/Workspaces/gsim/nbs/sim-data-meep-d7104144\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run on GDSFactory+ cloud\n",
+    "result = sim.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "21a5b5a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+    "result.plot_interactive(phase=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9458d977",
+   "metadata": {},
+   "source": [
+    "## 2D variational effective-index simulation\n",
+    "\n",
+    "A 2D FDTD (`sim.solver.is_3d = False`) collapses the z-dimension and is\n",
+    "10-100x faster. Instead of substituting bulk indices, varEIM derives the\n",
+    "in-plane permittivity from the vertical slab mode of the 3D stack\n",
+    "(Hammer & Ivanova 2009):\n",
+    "\n",
+    "$$\\varepsilon_\\mathrm{eff}(x,y) = n_\\mathrm{eff}^2(\\mathbf{r}) +\n",
+    "  \\frac{\\int dz\\,[\\varepsilon(x,y,z) - \\varepsilon(\\mathbf{r},z)]\\,\n",
+    "  |\\Phi_\\mathbf{r}(z)|^2}{\\int dz\\,|\\Phi_\\mathbf{r}(z)|^2}$$\n",
+    "\n",
+    "A Y-branch is a good candidate: it splits power by adiabatic mode evolution\n",
+    "along a propagating taper, which varEIM (accurate for propagation) captures\n",
+    "well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f547aff1",
+   "metadata": {},
+   "source": [
+    "### Effective indices from the vertical slab mode\n",
+    "\n",
+    "Evaluate at a core point on the input waveguide and a cladding point beside\n",
+    "it, both referenced to the same slab mode. The core returns the slab effective\n",
+    "index; the cladding is clamped to the physical oxide permittivity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "5a351fb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "n_core       = 2.8527  (bulk Si   = 3.47)\n",
+      "n_background = 1.4440  (bulk SiO2 = 1.44)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "from gsim.meep.eim import fit_medium, variational_effective_permittivity\n",
+    "\n",
+    "eim_sim = meep.Simulation()\n",
+    "eim_sim.geometry(component=c, stack=stack)\n",
+    "\n",
+    "core_point = (-7.0, 0.0)  # on the o1 input waveguide\n",
+    "background_point = (-7.0, 1.25)  # cladding beside the input waveguide\n",
+    "\n",
+    "eps_core = variational_effective_permittivity(core_point, core_point, eim_sim, 1.55)\n",
+    "eps_background = variational_effective_permittivity(\n",
+    "    background_point, core_point, eim_sim, 1.55\n",
+    ")\n",
+    "n_core = fit_medium(eps_core, 1.55)\n",
+    "n_background = fit_medium(eps_background, 1.55)\n",
+    "\n",
+    "print(f\"n_core       = {n_core:.4f}  (bulk Si   = 3.47)\")\n",
+    "print(f\"n_background = {n_background:.4f}  (bulk SiO2 = 1.44)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8cc19db",
+   "metadata": {},
+   "source": [
+    "### Configure and run the 2D simulation\n",
+    "\n",
+    "Same source, monitors, domain and solver settings as the 3D run, but\n",
+    "`is_3d=False` and the varEIM material indices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "a4de546a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stack validation: PASSED\n",
+      "Warnings:\n",
+      "  - Stopping: energy_decay (dt=20.0, decay_by=0.01, cap=2000.0)\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eim_sim.materials = {\n",
+    "    \"si\": Material(refractive_index=n_core),\n",
+    "    \"SiO2\": Material(refractive_index=n_background),\n",
+    "}\n",
+    "eim_sim.source(port=\"o1\", wavelength=1.55, wavelength_span=0.01)\n",
+    "eim_sim.monitors = [\"o1\", \"o2\", \"o3\"]\n",
+    "eim_sim.domain(pml=1.0, margin=0.5)\n",
+    "eim_sim.solver(resolution=25, simplify_tol=0.01, is_3d=False)\n",
+    "eim_sim.solver.stop_when_energy_decayed()\n",
+    "\n",
+    "print(eim_sim.validate_config())\n",
+    "eim_sim.plot_2d(slices=\"z\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "371f4fde",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  meep-8adf2e70  completed  0m 17s\n",
+      "Extracting results.tar.gz...\n",
+      "Downloaded 4 files to /Users/nath/Workspaces/gsim/nbs/sim-data-meep-8adf2e70\n"
+     ]
+    }
+   ],
+   "source": [
+    "result_eim = eim_sim.run()\n",
+    "result_eim.plot_interactive()\n",
+    "result_eim.show_animation()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7196a264",
+   "metadata": {},
+   "source": [
+    "### Compare 2D varEIM vs 3D\n",
+    "\n",
+    "Overlay the transmission into the two output arms (`S21`, `S31`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "32821a62",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "for key, color in [(\"S21\", \"C0\"), (\"S31\", \"C1\")]:\n",
+    "    if key in result_eim.s_params:\n",
+    "        ax.plot(\n",
+    "            result_eim.wavelengths,\n",
+    "            np.abs(result_eim.s_params[key]) ** 2,\n",
+    "            color + \"-\",\n",
+    "            label=f\"{key} 2D varEIM\",\n",
+    "        )\n",
+    "    if key in result.s_params:\n",
+    "        ax.plot(\n",
+    "            result.wavelengths,\n",
+    "            np.abs(result.s_params[key]) ** 2,\n",
+    "            color + \"--\",\n",
+    "            label=f\"{key} 3D\",\n",
+    "        )\n",
+    "ax.set_xlabel(\"wavelength (um)\")\n",
+    "ax.set_ylabel(\"|S|^2\")\n",
+    "ax.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fcb47864",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gsim (3.12.12)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/nbs/meep_ybranch_veim.py b/nbs/meep_ybranch_veim.py
new file mode 100644
index 00000000..6d3269bd
--- /dev/null
+++ b/nbs/meep_ybranch_veim.py
@@ -0,0 +1,180 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.19.2
+#   kernelspec:
+#     display_name: gsim (3.12.12)
+#     language: python
+#     name: python3
+# ---
+
+# %% [markdown]
+# # MEEP Y-branch: 3D and 2D variational EIM
+#
+# [MEEP](https://meep.readthedocs.io/) is an open-source FDTD electromagnetic
+# simulator. This notebook reproduces the 3D S-parameter simulation of the
+# photonic Y-branch from the [MEEP example](./meep_ybranch.py), then repeats it
+# as a fast **2D variational effective-index (varEIM)** simulation and compares
+# the two.
+#
+# **Requirements:**
+#
+# - UBC PDK: `uv pip install ubcpdk`
+# - [GDSFactory+](https://gdsfactory.com) account for cloud simulation
+
+# %% [markdown]
+# ### Load a pcell from UBC PDK
+
+# %%
+from ubcpdk import PDK, cells
+
+PDK.activate()
+
+c = cells.ebeam_y_1550()
+c
+
+# %% [markdown]
+# ### Configure and run the 3D simulation
+
+# %%
+from gsim import meep
+from gsim.common.stack import get_stack
+from gsim.meep.models.api import Material
+
+stack = get_stack()  # auto-detects active PDK
+
+sim = meep.Simulation()
+
+sim.geometry(component=c, stack=stack, z_crop="auto")
+sim.materials = {
+    "si": Material(refractive_index=3.47),
+    "SiO2": Material(refractive_index=1.44),
+}
+sim.source(port="o1", wavelength=1.55, wavelength_span=0.01)
+sim.monitors = ["o1", "o2", "o3"]
+sim.domain(pml=1.0, margin=0.5)
+sim.solver(resolution=20, simplify_tol=0.01, save_animation=True, verbose_interval=5.0)
+sim.solver.stop_when_energy_decayed()
+
+print(sim.validate_config())
+
+# %%
+sim.plot_2d(slices="xyz")
+
+# %% [markdown]
+# ### Run 3D simulation on cloud
+
+# %%
+# Run on GDSFactory+ cloud
+result = sim.run()
+
+# %%
+result.plot_interactive()
+
+# %%
+result.plot_interactive(phase=True)
+
+# %% [markdown]
+# ## 2D variational effective-index simulation
+#
+# A 2D FDTD (`sim.solver.is_3d = False`) collapses the z-dimension and is
+# 10-100x faster. Instead of substituting bulk indices, varEIM derives the
+# in-plane permittivity from the vertical slab mode of the 3D stack
+# (Hammer & Ivanova 2009):
+#
+# $$\varepsilon_\mathrm{eff}(x,y) = n_\mathrm{eff}^2(\mathbf{r}) +
+#   \frac{\int dz\,[\varepsilon(x,y,z) - \varepsilon(\mathbf{r},z)]\,
+#   |\Phi_\mathbf{r}(z)|^2}{\int dz\,|\Phi_\mathbf{r}(z)|^2}$$
+#
+# A Y-branch is a good candidate: it splits power by adiabatic mode evolution
+# along a propagating taper, which varEIM (accurate for propagation) captures
+# well.
+
+# %% [markdown]
+# ### Effective indices from the vertical slab mode
+#
+# Evaluate at a core point on the input waveguide and a cladding point beside
+# it, both referenced to the same slab mode. The core returns the slab effective
+# index; the cladding is clamped to the physical oxide permittivity.
+
+# %%
+import numpy as np
+
+from gsim.meep.eim import fit_medium, variational_effective_permittivity
+
+eim_sim = meep.Simulation()
+eim_sim.geometry(component=c, stack=stack)
+
+core_point = (-7.0, 0.0)  # on the o1 input waveguide
+background_point = (-7.0, 1.25)  # cladding beside the input waveguide
+
+eps_core = variational_effective_permittivity(core_point, core_point, eim_sim, 1.55)
+eps_background = variational_effective_permittivity(
+    background_point, core_point, eim_sim, 1.55
+)
+n_core = fit_medium(eps_core, 1.55)
+n_background = fit_medium(eps_background, 1.55)
+
+print(f"n_core       = {n_core:.4f}  (bulk Si   = 3.47)")
+print(f"n_background = {n_background:.4f}  (bulk SiO2 = 1.44)")
+
+# %% [markdown]
+# ### Configure and run the 2D simulation
+#
+# Same source, monitors, domain and solver settings as the 3D run, but
+# `is_3d=False` and the varEIM material indices.
+
+# %%
+eim_sim.materials = {
+    "si": Material(refractive_index=n_core),
+    "SiO2": Material(refractive_index=n_background),
+}
+eim_sim.source(port="o1", wavelength=1.55, wavelength_span=0.01)
+eim_sim.monitors = ["o1", "o2", "o3"]
+eim_sim.domain(pml=1.0, margin=0.5)
+eim_sim.solver(resolution=25, simplify_tol=0.01, is_3d=False)
+eim_sim.solver.stop_when_energy_decayed()
+
+print(eim_sim.validate_config())
+eim_sim.plot_2d(slices="z")
+
+# %%
+result_eim = eim_sim.run()
+result_eim.plot_interactive()
+result_eim.show_animation()
+
+# %% [markdown]
+# ### Compare 2D varEIM vs 3D
+#
+# Overlay the transmission into the two output arms (`S21`, `S31`).
+
+# %%
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots(figsize=(6, 4))
+for key, color in [("S21", "C0"), ("S31", "C1")]:
+    if key in result_eim.s_params:
+        ax.plot(
+            result_eim.wavelengths,
+            np.abs(result_eim.s_params[key]) ** 2,
+            color + "-",
+            label=f"{key} 2D varEIM",
+        )
+    if key in result.s_params:
+        ax.plot(
+            result.wavelengths,
+            np.abs(result.s_params[key]) ** 2,
+            color + "--",
+            label=f"{key} 3D",
+        )
+ax.set_xlabel("wavelength (um)")
+ax.set_ylabel("|S|^2")
+ax.legend()
+plt.tight_layout()
+plt.show()
+
+# %%
diff --git a/src/gsim/meep/eim.py b/src/gsim/meep/eim.py
new file mode 100644
index 00000000..0cfe21f7
--- /dev/null
+++ b/src/gsim/meep/eim.py
@@ -0,0 +1,281 @@
+"""Variational effective-index method (varEIM) for 2D FDTD.
+
+Computes a spatially-varying effective permittivity from a 3D layer stack,
+weighted by the vertical slab-mode intensity, so that a 2D (z-collapsed)
+simulation reproduces the vertical confinement that bulk-index substitution
+ignores.
+
+The effective permittivity at a lateral point ``(x, y)`` relative to a fixed
+reference point ``r`` is (Hammer & Ivanova 2009):
+
+    eps_eff(x, y) = n_eff^2(r)
+                    + integral[ (eps(x,y,z) - eps(r,z)) * |Phi_r(z)|^2 dz ]
+                      / integral[ |Phi_r(z)|^2 dz ]
+
+where ``n_eff(r)`` and ``Phi_r(z)`` are the effective index and field profile of
+the fundamental TE vertical slab mode at the reference point. In the core region
+the perturbation vanishes (``eps_eff = n_eff^2``); in the cladding it is
+``n_eff^2`` reduced by a mode-weighted average, giving the physically correct
+(reduced) lateral index contrast.
+
+This implementation is TE-only and uses a single reference mode, so it is valid
+only where the vertical mode profile is consistent across the device (strips,
+rings, MMIs). It breaks down for mode-converting transitions (e.g.
+strip-to-slot).
+
+Reference:
+    H. J. W. M. Hammer and O. V. Ivanova, "Effective index approximations of
+    photonic crystal slabs: a 2-to-1-D assessment," Opt. Quant. Electron. 41,
+    267 (2009).
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from gsim.common.cross_section import _layer_shapely_polys
+from gsim.common.stack.materials import resolve_material_at_wavelength
+
+if TYPE_CHECKING:
+    import gdsfactory as gf
+
+    from gsim.common.stack import LayerStack
+
+logger = logging.getLogger(__name__)
+
+
+def build_eps_z(
+    component: gf.Component,
+    layer_stack: LayerStack,
+    x: float,
+    y: float,
+    wavelength_um: float,
+    *,
+    nz: int = 400,
+    z_range: tuple[float, float] | None = None,
+    background_material: str = "air",
+) -> tuple[np.ndarray, np.ndarray]:
+    """Build the 1D vertical permittivity profile eps(z) at lateral point (x, y).
+
+    Samples the layer stack along z at a fixed ``(x, y)``: dielectrics provide
+    the background (cladding/box/substrate), patterned layers override the
+    background wherever their polygons cover the point. Where two patterned
+    layers overlap in z (e.g. core and slab), the higher-permittivity material
+    wins, matching gsim's "highest-index = core" convention.
+
+    Args:
+        component: gdsfactory Component (may contain references).
+        layer_stack: LayerStack describing layers and dielectrics.
+        x: Lateral X coordinate of the vertical cut (um).
+        y: Lateral Y coordinate of the vertical cut (um).
+        wavelength_um: Wavelength for material index lookup (um).
+        nz: Number of z samples (cell centers).
+        z_range: (zmin, zmax) for the cut. Defaults to ``layer_stack.get_z_range()``.
+        background_material: Material name for z samples not covered by any
+            dielectric or layer (typically the top air region).
+
+    Returns:
+        (z_grid, eps_z): the z sample coordinates (um) and permittivity at each.
+    """
+    from shapely.geometry import Point
+    from shapely.ops import unary_union
+
+    # get_polygons(merge=True) mutates, which is disabled on locked cached
+    # cells; work on an unlocked copy.
+    if getattr(component, "locked", False):
+        component = component.copy()
+
+    z_lo, z_hi = z_range if z_range is not None else layer_stack.get_z_range()
+    dz = (z_hi - z_lo) / nz
+    z_grid = z_lo + (np.arange(nz) + 0.5) * dz
+
+    eps_cache: dict[str, float] = {}
+
+    def eps_of(material: str) -> float:
+        if material not in eps_cache:
+            resolved = resolve_material_at_wavelength(material, wavelength_um)
+            eps = None if resolved is None else resolved.permittivity_scalar
+            eps_cache[material] = float(eps) if eps is not None else 1.0
+        return eps_cache[material]
+
+    # Background pass: fill from dielectrics, fall back to background_material.
+    eps_z = np.full(nz, eps_of(background_material), dtype=float)
+    for d in sorted(layer_stack.dielectrics, key=lambda d: d["zmin"]):
+        band = (z_grid >= d["zmin"]) & (z_grid < d["zmax"])
+        eps_z[band] = eps_of(d["material"])
+
+    # Coverage pass: patterned layers override the background where they cover
+    # (x, y); on overlapping z bands the higher-permittivity layer wins.
+    pt = Point(x, y)
+    dbu = getattr(getattr(component, "kcl", None), "dbu", 0.001)
+    for layer in layer_stack.layers.values():
+        gds_layer = getattr(layer, "gds_layer", None)
+        if gds_layer is None:
+            continue
+        gds_layer_tuple = (int(gds_layer[0]), int(gds_layer[1]))
+        polys = _layer_shapely_polys(component, gds_layer_tuple, dbu)
+        if not polys:
+            continue
+        if not unary_union(polys).covers(pt):
+            continue
+        eps_layer = eps_of(layer.material)
+        band = (z_grid >= layer.zmin) & (z_grid < layer.zmax)
+        eps_z[band] = np.maximum(eps_z[band], eps_layer)
+
+    return z_grid, eps_z
+
+
+def solve_slab_mode(
+    z_grid: np.ndarray,
+    eps_z: np.ndarray,
+    wavelength_um: float,
+) -> tuple[float, np.ndarray]:
+    """Solve the fundamental TE vertical slab mode of a 1D permittivity profile.
+
+    Solves the 1D Helmholtz eigenproblem on a uniform z grid::
+
+        d^2 Phi / dz^2 + k0^2 eps(z) Phi = k0^2 n_eff^2 Phi
+
+    with Dirichlet boundaries (Phi -> 0 at the domain edges). The largest
+    eigenvalue is ``n_eff^2`` and its eigenvector is the field profile ``Phi``.
+
+    Args:
+        z_grid: Uniform z sample coordinates (um).
+        eps_z: Permittivity at each z sample.
+        wavelength_um: Wavelength (um).
+
+    Returns:
+        (n_eff, Phi): effective index and the (L2-normalized) field profile,
+        sampled on ``z_grid``.
+    """
+    from scipy.linalg import eigh_tridiagonal
+
+    dz = float(z_grid[1] - z_grid[0])
+    k0 = 2.0 * np.pi / wavelength_um
+
+    # A = (1/k0^2) D2 + diag(eps), with D2 the standard 2nd-derivative stencil.
+    off = (1.0 / k0**2) * (1.0 / dz**2) * np.ones(len(z_grid) - 1)
+    diag = (1.0 / k0**2) * (-2.0 / dz**2) + eps_z
+
+    # Fundamental mode = largest eigenvalue (= n_eff^2).
+    n = len(z_grid)
+    eigvals, eigvecs = eigh_tridiagonal(
+        diag, off, select="i", select_range=(n - 1, n - 1)
+    )
+    n_eff = float(np.sqrt(eigvals[-1]))
+
+    phi = eigvecs[:, -1]
+    phi = phi / np.sqrt(np.trapezoid(phi**2, z_grid))
+    return n_eff, phi
+
+
+def _cladding_eps(layer_stack, z_value: float, wavelength_um: float) -> float:
+    """Permittivity of the background dielectric covering ``z_value``.
+
+    This is the physical cladding the guiding layer is embedded in (e.g. the
+    oxide around an SOI strip), used as the floor for the variational result.
+    """
+    for d in layer_stack.dielectrics:
+        if d["zmin"] <= z_value < d["zmax"]:
+            resolved = resolve_material_at_wavelength(d["material"], wavelength_um)
+            if resolved is not None and resolved.permittivity_scalar is not None:
+                return float(resolved.permittivity_scalar)
+    return 1.0
+
+
+def variational_effective_permittivity(
+    xy: tuple[float, float],
+    reference_xy: tuple[float, float],
+    sim,
+    wavelength: float = 1.55,
+    *,
+    nz: int = 400,
+    eps_floor: float | None = None,
+) -> float:
+    """Variational effective permittivity at ``xy`` relative to ``reference_xy``.
+
+    Implements the Hammer & Ivanova formula by solving the vertical slab mode at
+    ``reference_xy`` and weighting the local permittivity difference by the mode
+    intensity. Geometry (component + layer stack) is taken from ``sim``.
+
+    Args:
+        xy: (x, y) location where the effective permittivity is evaluated.
+        reference_xy: (x, y) reference point for the slab mode. Must lie in a
+            single-mode guiding region; the same reference is used for every
+            ``xy`` to keep the perturbation consistent.
+        sim: A ``gsim.meep.Simulation`` carrying ``sim.geometry.component`` and
+            ``sim.geometry.stack``.
+        wavelength: Wavelength in um.
+        nz: Number of z samples for the profiles and slab solve.
+        eps_floor: Lower floor on the returned permittivity. For points whose
+            vertical column cannot support the reference mode (e.g. pure
+            cladding under a strongly confined strip mode) the raw variational
+            value can drop near zero or negative. ``None`` (default) clamps to
+            the physical cladding permittivity at the guiding plane (e.g. oxide)
+            so evanescent behaviour in gaps stays physical; pass a float to
+            override.
+
+    Returns:
+        Effective permittivity at ``xy`` (square it back for index:
+        ``n = sqrt(eps)``).
+    """
+    component = sim.geometry.component
+    layer_stack = sim.geometry.stack
+
+    z_grid, eps_reference = build_eps_z(
+        component, layer_stack, reference_xy[0], reference_xy[1], wavelength, nz=nz
+    )
+    n_eff, phi = solve_slab_mode(z_grid, eps_reference, wavelength)
+
+    z_grid, eps_local = build_eps_z(
+        component, layer_stack, xy[0], xy[1], wavelength, nz=nz
+    )
+
+    intensity = np.abs(phi) ** 2
+    weighted_shift = np.trapezoid((eps_local - eps_reference) * intensity, z_grid)
+    mode_power = np.trapezoid(intensity, z_grid)
+    eps = n_eff**2 + weighted_shift / mode_power
+
+    if eps_floor is None:
+        z_guiding = float(z_grid[np.argmax(intensity)])
+        eps_floor = _cladding_eps(layer_stack, z_guiding, wavelength)
+    return max(float(eps), eps_floor)
+
+
+def fit_medium(
+    eps_eff_values: float | list[float],
+    wavelengths: float | list[float],
+) -> float:
+    """Convert effective permittivity to a constant effective index (stub).
+
+    Single-wavelength, zero-dispersion placeholder: returns ``sqrt(eps_eff)`` so
+    the value can be dropped straight into ``sim.materials``. The dispersive fit
+    (eps_eff(lambda) -> Sellmeier -> Lorentzian poles via
+    ``gsim.meep.materials.sellmeier_to_lorentzian_poles``) replaces this later.
+
+    Args:
+        eps_eff_values: One effective permittivity, or a list (only length 1 is
+            supported until dispersive fitting lands).
+        wavelengths: The matching wavelength(s) in um.
+
+    Returns:
+        Constant effective refractive index ``n = sqrt(eps_eff)``.
+    """
+    # TODO(#156): fit eps_eff(lambda) to a dispersive MEEP-compatible model.
+    eps_list = (
+        [eps_eff_values]
+        if isinstance(eps_eff_values, (int, float))
+        else list(eps_eff_values)
+    )
+    wl_list = (
+        [wavelengths] if isinstance(wavelengths, (int, float)) else list(wavelengths)
+    )
+    if len(eps_list) != 1 or len(wl_list) != 1:
+        raise NotImplementedError(
+            "Dispersive fitting is not implemented yet; pass a single "
+            "(eps_eff, wavelength) pair (zero-dispersion stub)."
+        )
+    return float(np.sqrt(eps_list[0]))