diff --git a/MMIPowerSplitter2x2.ipynb b/MMIPowerSplitter2x2.ipynb
new file mode 100644
index 00000000..fdea651f
--- /dev/null
+++ b/MMIPowerSplitter2x2.ipynb
@@ -0,0 +1,868 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2x2 MMI power splitter\n",
+    "\n",
+    "**Note: this notebook is 90% AI-generated.**\n",
+    "\n",
+    "In this notebook, we will design and simulate a 2x2 Multi-Mode Interference (MMI) power splitter for photonic integrated circuits (PICs). The MMI power splitter is a fundamental component in integrated photonics, providing equal power splitting (50/50) with high efficiency.\n",
+    "\n",
+    "The design follows standard Silicon-on-Insulator (SOI) platform specifications with cosine S-bends for optimal performance. We will demonstrate the complete simulation workflow including geometry setup and performance evaluation.\n",
+    "\n",
+    "For more integrated photonic examples such as the 8-Channel mode and polarization demultiplexer, the broadband bi-level taper polarization rotator-splitter, and the broadband directional coupler, please visit our [examples page](https://www.flexcompute.com/tidy3d/examples/).\n",
+    "\n",
+    "If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101 tutorials](https://www.flexcompute.com/tidy3d/learning-center/fdtd-101/).\n",
+    "\n",
+    "FDTD simulations can diverge due to various reasons. If you run into any simulation divergence issues, please follow the steps outlined in our [troubleshooting guide](https://www.flexcompute.com/tidy3d/learning-center/troubleshooting/) to resolve it.\n",
+    "\n",
+    "<img src=\"./img/mmi_power_splitter_2x2.png\" alt=\"Schematic of the 2x2 MMI Power Splitter\"  width=\"400\"/>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Standard Python Imports\n",
+    "\n",
+    "We start by importing the necessary libraries for our simulation.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standard python imports.\n",
+    "import gdstk\n",
+    "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# Import regular tidy3d.\n",
+    "import tidy3d as td\n",
+    "import tidy3d.web as web"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MMI Power Splitter Structure\n",
+    "\n",
+    "The 2x2 MMI power splitter is based on a standard Silicon-on-Insulator (SOI) wafer with a device layer of 220 nm. The MMI section provides multimode interference for equal power splitting, while cosine S-bends route the input/output signals to offset positions for practical device integration.\n",
+    "\n",
+    "**Key Design Parameters:**\n",
+    "- *Material Platform*: Silicon-on-Insulator (SOI)\n",
+    "- *Wavelength Range*: 1.5 to 1.6 μm (telecom C-band)\n",
+    "- *Design Goal*: Equal power splitting (50/50)\n",
+    "\n",
+    "The MMI length is designed to achieve optimal interference patterns for equal power splitting around 1.55 micrometers, while the S-bends provide smooth transitions to offset input/output waveguides.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# MMI power splitter setup.\n",
+    "wavelength = 1.55  # Center simulation wavelength (um).\n",
+    "freq0 = td.C_0 / wavelength  # Central frequency."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Materials.\n",
+    "n_si = 3.48  # Silicon refractive index.\n",
+    "n_sio2 = 1.44  # SiO2 refractive index.\n",
+    "\n",
+    "# Material definitions.\n",
+    "mat_si = td.Medium(permittivity=n_si**2)  # Silicon waveguide material.\n",
+    "mat_sio2 = td.Medium(permittivity=n_sio2**2)  # SiO2 substrate material."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This cell contains all the geometry, sources, monitors, and simulation creation\n",
+    "\n",
+    "# MMI power splitter dimensions.\n",
+    "w_wg = 0.4  # Waveguide width (um).\n",
+    "h_si = 0.22  # Silicon layer height (um).\n",
+    "w_mmi = 1.0  # MMI section width (um).\n",
+    "l_mmi = 3.0  # MMI section length (um).\n",
+    "gap = 0.2  # Gap between output waveguides (um).\n",
+    "\n",
+    "# Waveguide lengths.\n",
+    "l_input = 2.0  # Input waveguide length (um).\n",
+    "l_output = 3.0  # Output waveguide length (um).\n",
+    "\n",
+    "# S-bend parameters.\n",
+    "s_bend_offset = 1.0  # Lateral offset for S-bends (um).\n",
+    "s_bend_length = 4.0  # Length of S-bend section (um)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cosine S-bend Function\n",
+    "\n",
+    "We define a cosine S-bend function for smooth waveguide transitions. This function creates high-quality S-bends that minimize insertion loss.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to create cosine S-bends.\n",
+    "def cosine_sbend(\n",
+    "    x0,\n",
+    "    y0,\n",
+    "    z0,\n",
+    "    wg_width,\n",
+    "    wg_thickness,\n",
+    "    bend_length,\n",
+    "    bend_height,\n",
+    "    medium,\n",
+    "    orientation=\"x\",\n",
+    "    mirror=False,\n",
+    "    sidewall_angle=0,\n",
+    "):\n",
+    "    \"\"\"Cosine S-bend function for smooth waveguide transitions.\"\"\"\n",
+    "    cell = gdstk.Cell(\"bend\")\n",
+    "    path = gdstk.RobustPath((x0, y0), wg_width, layer=1, datatype=0)\n",
+    "\n",
+    "    if orientation == \"x\":\n",
+    "        path.segment(\n",
+    "            (x0 + bend_length, y0),\n",
+    "            offset=lambda u: -bend_height * np.cos(np.pi * u) / 2 + bend_height / 2,\n",
+    "        )\n",
+    "        if mirror:\n",
+    "            path.mirror((x0 + 1, y0), (x0, y0))\n",
+    "    elif orientation == \"y\":\n",
+    "        path.segment(\n",
+    "            (x0, y0 + bend_length),\n",
+    "            offset=lambda u: -bend_height * np.cos(np.pi * u) / 2 + bend_height / 2,\n",
+    "        )\n",
+    "        if mirror:\n",
+    "            path.mirror((x0, y0 + 1), (x0, y0))\n",
+    "\n",
+    "    cell.add(path)\n",
+    "    bend_geo = td.PolySlab.from_gds(\n",
+    "        cell,\n",
+    "        gds_layer=1,\n",
+    "        axis=2,\n",
+    "        slab_bounds=(z0 - wg_thickness / 2, z0 + wg_thickness / 2),\n",
+    "        sidewall_angle=sidewall_angle,\n",
+    "    )\n",
+    "    return td.Structure(geometry=bend_geo[0], medium=medium)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Complete MMI Power Splitter Simulation Setup\n",
+    "\n",
+    "Now we create the complete 2x2 MMI power splitter simulation with x-axis propagation. The simulation includes the MMI section, input/output waveguides, cosine S-bends, sources, and monitors.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate optimized simulation domain size for x-axis propagation.\n",
+    "x_min = -1.5 - 3.0  # -4.5 um\n",
+    "x_max = 5.5 + 3.0  # 8.5 um\n",
+    "total_length = x_max - x_min  # 13.0 um\n",
+    "total_width = w_mmi + 4.0  # Keep width buffer for offset waveguides\n",
+    "total_height = 2.0  # Sufficient for SOI structure\n",
+    "sim_size_optimized = (total_length, total_width, total_height)\n",
+    "\n",
+    "# 1. MMI section (main multimode interference region).\n",
+    "mmi_section = td.Structure(\n",
+    "    geometry=td.Box(center=(0, 0, 0), size=(l_mmi, w_mmi, h_si)), medium=mat_si\n",
+    ")\n",
+    "\n",
+    "# 2. Output S-bend 1.\n",
+    "output_s_bend_1 = cosine_sbend(\n",
+    "    x0=l_mmi / 2,  # Start at MMI right edge (x-axis propagation)\n",
+    "    y0=w_wg / 2 + gap / 2,  # Start at MMI top edge\n",
+    "    z0=0,\n",
+    "    wg_width=w_wg,\n",
+    "    wg_thickness=h_si,\n",
+    "    bend_length=s_bend_length,\n",
+    "    bend_height=s_bend_offset,\n",
+    "    medium=mat_si,\n",
+    "    orientation=\"x\",\n",
+    "    mirror=False,\n",
+    ")\n",
+    "\n",
+    "# 3. Output waveguide 1.\n",
+    "output_wg_1 = td.Structure(\n",
+    "    geometry=td.Box(\n",
+    "        center=(l_mmi / 2 + s_bend_length + l_output / 2, w_wg / 2 + gap / 2 + s_bend_offset, 0),\n",
+    "        size=(l_output, w_wg, h_si),\n",
+    "    ),\n",
+    "    medium=mat_si,\n",
+    ")\n",
+    "\n",
+    "# 4. Output S-bend 2.\n",
+    "output_s_bend_2 = cosine_sbend(\n",
+    "    x0=l_mmi / 2,  # Start at MMI right edge (x-axis propagation)\n",
+    "    y0=-w_wg / 2 - gap / 2,  # Start at MMI bottom edge\n",
+    "    z0=0,\n",
+    "    wg_width=w_wg,\n",
+    "    wg_thickness=h_si,\n",
+    "    bend_length=s_bend_length,\n",
+    "    bend_height=-s_bend_offset,\n",
+    "    medium=mat_si,\n",
+    "    orientation=\"x\",\n",
+    "    mirror=False,\n",
+    ")\n",
+    "\n",
+    "# 5. Output waveguide 2.\n",
+    "output_wg_2 = td.Structure(\n",
+    "    geometry=td.Box(\n",
+    "        center=(l_mmi / 2 + s_bend_length + l_output / 2, -w_wg / 2 - gap / 2 - s_bend_offset, 0),\n",
+    "        size=(l_output, w_wg, h_si),\n",
+    "    ),\n",
+    "    medium=mat_si,\n",
+    ")\n",
+    "\n",
+    "# 6. Create input waveguides using Tidy3D reflection transformation for x-axis propagation.\n",
+    "input_wg_1_geometry = output_wg_1.geometry.reflected(normal=(1, 0, 0))\n",
+    "input_wg_1 = td.Structure(geometry=input_wg_1_geometry, medium=mat_si)\n",
+    "input_wg_2_geometry = output_wg_2.geometry.reflected(normal=(1, 0, 0))\n",
+    "input_wg_2 = td.Structure(geometry=input_wg_2_geometry, medium=mat_si)\n",
+    "input_s_bend_1_geometry = output_s_bend_1.geometry.reflected(normal=(1, 0, 0))\n",
+    "input_s_bend_1 = td.Structure(geometry=input_s_bend_1_geometry, medium=mat_si)\n",
+    "input_s_bend_2_geometry = output_s_bend_2.geometry.reflected(normal=(1, 0, 0))\n",
+    "input_s_bend_2 = td.Structure(geometry=input_s_bend_2_geometry, medium=mat_si)\n",
+    "\n",
+    "# 7. Combine all structures.\n",
+    "mmi_structures = [\n",
+    "    input_s_bend_1,\n",
+    "    input_wg_1,\n",
+    "    input_s_bend_2,\n",
+    "    input_wg_2,\n",
+    "    mmi_section,\n",
+    "    output_s_bend_1,\n",
+    "    output_wg_1,\n",
+    "    output_s_bend_2,\n",
+    "    output_wg_2,\n",
+    "]\n",
+    "\n",
+    "# 8. Create frequency array for wavelength sweep.\n",
+    "wavelengths = np.arange(1.5, 1.61, 0.01)  # 1.5 to 1.6 μm with 10 nm steps\n",
+    "frequencies = td.C_0 / wavelengths\n",
+    "\n",
+    "# 9. Mode source at the upper input waveguide (x-axis propagation).\n",
+    "source_position = (-6, 1.3, 0)\n",
+    "source_size = (0, 6 * w_wg, 6 * h_si)\n",
+    "mode_source = td.ModeSource(\n",
+    "    center=source_position,\n",
+    "    size=source_size,\n",
+    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10),\n",
+    "    direction=\"+\",\n",
+    "    mode_spec=td.ModeSpec(num_modes=1),\n",
+    "    mode_index=0,\n",
+    ")\n",
+    "\n",
+    "# 10. Mode monitors at the output waveguides (x-axis propagation).\n",
+    "monitor_1_position = (l_mmi / 2 + s_bend_length + 0.5, w_wg / 2 + gap / 2 + s_bend_offset, 0)\n",
+    "monitor_1_size = (0, 6 * w_wg, 6 * h_si)\n",
+    "mode_monitor_1 = td.ModeMonitor(\n",
+    "    center=monitor_1_position,\n",
+    "    size=monitor_1_size,\n",
+    "    freqs=frequencies,\n",
+    "    mode_spec=td.ModeSpec(num_modes=1),\n",
+    "    name=\"mode_output_1\",\n",
+    ")\n",
+    "\n",
+    "monitor_2_position = (l_mmi / 2 + s_bend_length + 0.5, -w_wg / 2 - gap / 2 - s_bend_offset, 0)\n",
+    "monitor_2_size = (0, 6 * w_wg, 6 * h_si)\n",
+    "mode_monitor_2 = td.ModeMonitor(\n",
+    "    center=monitor_2_position,\n",
+    "    size=monitor_2_size,\n",
+    "    freqs=frequencies,\n",
+    "    mode_spec=td.ModeSpec(num_modes=1),\n",
+    "    name=\"mode_output_2\",\n",
+    ")\n",
+    "\n",
+    "# 11. Field monitor at xy plane - record fields at specific wavelengths (x-axis propagation).\n",
+    "field_freqs = [td.C_0 / 1.55, td.C_0 / 1.58]  # 1.55 and 1.58 um\n",
+    "field_monitor = td.FieldMonitor(\n",
+    "    center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=field_freqs, name=\"field_xy\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 12. Create the complete simulation.\n",
+    "sim_mmi = td.Simulation(\n",
+    "    size=sim_size_optimized,\n",
+    "    structures=mmi_structures,\n",
+    "    sources=[mode_source],\n",
+    "    monitors=[mode_monitor_1, mode_monitor_2, field_monitor],\n",
+    "    run_time=1e-12,\n",
+    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
+    "    medium=mat_sio2,\n",
+    "    grid_spec=td.GridSpec(\n",
+    "        grid_x=td.AutoGrid(min_steps_per_wvl=20),\n",
+    "        grid_y=td.AutoGrid(min_steps_per_wvl=20),\n",
+    "        grid_z=td.AutoGrid(min_steps_per_wvl=20),\n",
+    "        wavelength=wavelength,\n",
+    "    ),\n",
+    "    symmetry=(0, 0, 0),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation Visualization\n",
+    "\n",
+    "Let's visualize the complete MMI power splitter setup to verify the geometry and propagation direction.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Simulation Setup Visualization - XY Setup Plane\n",
+    "fig_xy = sim_mmi.plot(z=0)  # XY cross-section at z=0 (MMI center) - shows complete setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"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\" ></div>\n",
+       "    <script>\n",
+       "        \n",
+       "        /**\n",
+       "        * Simulation Viewer Injector\n",
+       "        *\n",
+       "        * Monitors the document for elements being added in the form:\n",
+       "        *\n",
+       "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
+       "        *\n",
+       "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
+       "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
+       "        * configuration of the first started script (e.g. viewer URL) applying.\n",
+       "        *\n",
+       "        */\n",
+       "        (function() {\n",
+       "            const TARGET_CLASS = \"simulation-viewer\";\n",
+       "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
+       "            const VIEWER_URL = \"https://tidy3d.simulation.cloud/simulation-viewer\";\n",
+       "\n",
+       "            class SimulationViewerInjector {\n",
+       "                constructor() {\n",
+       "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
+       "                        this.injectViewer(node);\n",
+       "                    }\n",
+       "\n",
+       "                    // Monitor for newly added nodes to the DOM\n",
+       "                    this.observer = new MutationObserver(this.onMutations.bind(this));\n",
+       "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
+       "                }\n",
+       "\n",
+       "                onMutations(mutations) {\n",
+       "                    for (var mutation of mutations) {\n",
+       "                        if (mutation.type === 'childList') {\n",
+       "                            /**\n",
+       "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
+       "                            * It may be the case that setting content with innerHTML does not trigger.\n",
+       "                            *\n",
+       "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
+       "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
+       "                            */\n",
+       "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
+       "                            for (var node of viewers) {\n",
+       "                                this.injectViewer(node);\n",
+       "                            }\n",
+       "                        }\n",
+       "                    }\n",
+       "                }\n",
+       "\n",
+       "                injectViewer(node) {\n",
+       "                    // (re-)check that this is a valid simulation container and has not already been injected\n",
+       "                    if (node.classList.contains(TARGET_CLASS) && !node.classList.contains(ACTIVE_CLASS)) {\n",
+       "                        // Mark node as injected, to prevent re-runs\n",
+       "                        node.classList.add(ACTIVE_CLASS);\n",
+       "\n",
+       "                        var uuid;\n",
+       "                        if (window.crypto && window.crypto.randomUUID) {\n",
+       "                            uuid = window.crypto.randomUUID();\n",
+       "                        } else {\n",
+       "                            uuid = \"\" + Math.random();\n",
+       "                        }\n",
+       "\n",
+       "                        var frame = document.createElement(\"iframe\");\n",
+       "                        frame.width = node.dataset.width || 800;\n",
+       "                        frame.height = node.dataset.height || 800;\n",
+       "                        frame.style.cssText = `width:${frame.width}px;height:${frame.height}px;max-width:none;border:0;display:block`\n",
+       "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
+       "\n",
+       "                        var postMessageToViewer;\n",
+       "                        postMessageToViewer = event => {\n",
+       "                            if(event.data.type === 'viewer' && event.data.uuid===uuid){\n",
+       "                                frame.contentWindow.postMessage({ type: 'jupyter', uuid, value: node.dataset.simulation, fileType: 'hdf5'}, '*');\n",
+       "\n",
+       "                                // Run once only\n",
+       "                                window.removeEventListener('message', postMessageToViewer);\n",
+       "                            }\n",
+       "                        };\n",
+       "                        window.addEventListener(\n",
+       "                            'message',\n",
+       "                            postMessageToViewer,\n",
+       "                            false\n",
+       "                        );\n",
+       "\n",
+       "                        node.appendChild(frame);\n",
+       "                    }\n",
+       "                }\n",
+       "            }\n",
+       "\n",
+       "            if (!window.simulationViewerInjector) {\n",
+       "                window.simulationViewerInjector = new SimulationViewerInjector();\n",
+       "            }\n",
+       "        })();\n",
+       "    \n",
+       "    </script>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 3D Visualization\n",
+    "fig_3d = sim_mmi.plot_3d()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cost Estimation\n",
+    "\n",
+    "Before running the simulation, let's estimate the computational cost."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:09:10 -03 </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'mmi_2x2_cost_estimation'</span> with task_id                \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-4e41a63a-22fd-4da6-9504-1b43e0088728'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m13:09:10 -03\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'mmi_2x2_cost_estimation'\u001b[0m with task_id                \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-4e41a63a-22fd-4da6-9504-1b43e0088728'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4e41a63a-22fd-4da6-9504-1b43e0088728\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-4e41a63a-22f</span></a>\n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-4e41a63a-22fd-4da6-9504-1b43e0088728\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">d-4da6-9504-1b43e0088728'</span></a>.                                         \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=753610;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4e41a63a-22fd-4da6-9504-1b43e0088728\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=357789;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4e41a63a-22fd-4da6-9504-1b43e0088728\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=753610;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4e41a63a-22fd-4da6-9504-1b43e0088728\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=549517;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4e41a63a-22fd-4da6-9504-1b43e0088728\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=753610;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4e41a63a-22fd-4da6-9504-1b43e0088728\u001b\\\u001b[32m-4e41a63a-22f\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=753610;https://tidy3d.simulation.cloud/workbench?taskId=fdve-4e41a63a-22fd-4da6-9504-1b43e0088728\u001b\\\u001b[32md-4da6-9504-1b43e0088728'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/315b8f7e-fef5-4f8e-a0be-4c5a6c6f6194\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=41272;https://tidy3d.simulation.cloud/folders/315b8f7e-fef5-4f8e-a0be-4c5a6c6f6194\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e77c17d054e4df1b88ff8dccbdd914c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:09:13 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.077</span>. Minimum cost depends on task       \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m13:09:13 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.077\u001b[0m. Minimum cost depends on task       \n",
+       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
+       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:09:14 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.077</span>. Minimum cost depends on task       \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed  \n",
+       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>FlexCredit cost after a simulation run.                            \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m13:09:14 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.077\u001b[0m. Minimum cost depends on task       \n",
+       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
+       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Estimated cost: 0.0774 Flex Credits\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Cost Estimation\n",
+    "job = web.Job(simulation=sim_mmi, task_name=\"mmi_2x2_cost_estimation\", verbose=True)\n",
+    "cost_info = web.estimate_cost(job.task_id)\n",
+    "print(f\"Estimated cost: {cost_info:.4f} Flex Credits\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run Simulation\n",
+    "\n",
+    "Now we run the simulation to obtain the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:09:15 -03 </span>status = success                                                   \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m13:09:15 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac879db427ed49e194140e55d6850cc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">13:09:19 -03 </span>loading simulation from mmi_2x2_results.hdf5                       \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m13:09:19 -03\u001b[0m\u001b[2;36m \u001b[0mloading simulation from mmi_2x2_results.hdf5                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Run Simulation\n",
+    "sim_data = job.run(path=\"mmi_2x2_results.hdf5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Result Analysis\n",
+    "\n",
+    "Let's analyze the simulation results to evaluate the MMI power splitter performance.\n",
+    "\n",
+    "**Key Performance Metrics:**\n",
+    "\n",
+    "- **Power Splitting Ratio**: The fraction of total power that goes to each output port. For an ideal 2x2 MMI power splitter, both outputs should have a splitting ratio of 0.5 (50/50 split).\n",
+    "\n",
+    "- **Total Power**: The sum of power from both output ports, showing the overall transmission efficiency of the device."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average splitting ratio 1: 0.452\n",
+      "Average splitting ratio 2: 0.548\n",
+      "Average total power: 0.845\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract mode coefficients from ModeMonitors\n",
+    "mode_coeff_1 = sim_data[\"mode_output_1\"].amps.sel(mode_index=0, direction=\"+\")\n",
+    "mode_coeff_2 = sim_data[\"mode_output_2\"].amps.sel(mode_index=0, direction=\"+\")\n",
+    "\n",
+    "# Calculate power splitting\n",
+    "power_1 = np.abs(mode_coeff_1) ** 2\n",
+    "power_2 = np.abs(mode_coeff_2) ** 2\n",
+    "total_power = power_1 + power_2\n",
+    "\n",
+    "# Calculate splitting ratio\n",
+    "splitting_ratio_1 = power_1 / total_power\n",
+    "splitting_ratio_2 = power_2 / total_power\n",
+    "\n",
+    "# Ensure arrays are numpy arrays for consistent indexing\n",
+    "splitting_ratio_1 = np.array(splitting_ratio_1)\n",
+    "splitting_ratio_2 = np.array(splitting_ratio_2)\n",
+    "total_power = np.array(total_power)\n",
+    "\n",
+    "# Create comprehensive analysis plots\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "\n",
+    "# Power splitting vs wavelength\n",
+    "ax1.plot(wavelengths, splitting_ratio_1, \"b-\", label=\"Output 1\", linewidth=2)\n",
+    "ax1.plot(wavelengths, splitting_ratio_2, \"r-\", label=\"Output 2\", linewidth=2)\n",
+    "ax1.axhline(y=0.5, color=\"k\", linestyle=\"--\", alpha=0.7, label=\"Ideal 50/50\")\n",
+    "ax1.set_xlabel(\"Wavelength (μm)\")\n",
+    "ax1.set_ylabel(\"Power Splitting Ratio\")\n",
+    "ax1.set_title(\"MMI Power Splitting vs Wavelength\")\n",
+    "ax1.legend()\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Total power vs wavelength\n",
+    "ax2.plot(wavelengths, total_power, \"g-\", linewidth=2)\n",
+    "ax2.set_xlabel(\"Wavelength (μm)\")\n",
+    "ax2.set_ylabel(\"Total Power\")\n",
+    "ax2.set_title(\"Total Transmitted Power vs Wavelength\")\n",
+    "ax2.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Print key performance metrics\n",
+    "print(f\"Average splitting ratio 1: {splitting_ratio_1.mean():.3f}\")\n",
+    "print(f\"Average splitting ratio 2: {splitting_ratio_2.mean():.3f}\")\n",
+    "print(f\"Average total power: {total_power.mean():.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Power Splitting Performance Analysis:**\n",
+    "\n",
+    "The power splitting ratio plot reveals several important characteristics of the MMI power splitter:\n",
+    "\n",
+    "- **Optimal Operating Wavelength**: The device achieves perfect 50/50 splitting at approximately 1.58 μm, where both output curves intersect the ideal 50/50 line.\n",
+    "\n",
+    "- **Wavelength Dependence**: The splitting ratio shows significant wavelength dependence:\n",
+    "  - At shorter wavelengths (1.50 μm): Output 2 receives ~62% of power, Output 1 receives ~37%\n",
+    "  - At longer wavelengths (1.60 μm): Output 1 receives ~54% of power, Output 2 receives ~46%\n",
+    "\n",
+    "- **Design Implications**: This wavelength dependence is typical for MMI devices and can be optimized by adjusting the MMI length, width, or using apodization techniques for broader bandwidth performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Field Intensity Analysis\n",
+    "\n",
+    "Finally, let's examine the field intensity distribution at specific wavelengths in the XY plane to understand the light propagation through the MMI."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x300 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a 2x1 subplot for the two wavelengths.\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 3))\n",
+    "\n",
+    "# Plot field intensity at 1.55 um using Tidy3D's built-in method.\n",
+    "sim_data.plot_field(\"field_xy\", \"E\", val=\"abs^2\", f=td.C_0 / 1.55, ax=axes[0])\n",
+    "axes[0].set_title(\"Field Intensity at 1.55 μm (XY plane)\")\n",
+    "\n",
+    "# Plot field intensity at 1.58 um using Tidy3D's built-in method.\n",
+    "sim_data.plot_field(\"field_xy\", \"E\", val=\"abs^2\", f=td.C_0 / 1.58, ax=axes[1])\n",
+    "axes[1].set_title(\"Field Intensity at 1.58 μm (XY plane)\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "applications": [
+   "Passive photonic integrated circuit components"
+  ],
+  "description": "This notebook demonstrates the FDTD simulation of a 2x2 MMI Power Splitter using Tidy3D.",
+  "feature_image": "./img/mmi_power_splitter_2x2.png",
+  "features": [
+   "Geometry transformation"
+  ],
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "keywords": "mmi, power splitter, photonic integrated circuits, Tidy3D, FDTD",
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  },
+  "title": "2x2 MMI Power Splitter | Flexcompute"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/case_studies/pic.rst b/docs/case_studies/pic.rst
index 237bcd9d..9e382eb0 100644
--- a/docs/case_studies/pic.rst
+++ b/docs/case_studies/pic.rst
@@ -16,6 +16,7 @@ Passive photonic integrated circuit (PIC) components form the backbone of many o
     ../../GratingCoupler
     ../../FocusedApodGC
     ../../MMI1x4
+    ../../MMIPowerSplitter2x2
     ../../RingResonator
     ../../BilevelPSR
     ../../WaveguideSizeConverter
diff --git a/img/mmi_power_splitter_2x2.png b/img/mmi_power_splitter_2x2.png
new file mode 100644
index 00000000..8ff19748
Binary files /dev/null and b/img/mmi_power_splitter_2x2.png differ