diff --git a/book/pages/layout_demo.ipynb b/book/pages/layout_demo.ipynb
index 0e34751..7dc6b43 100644
--- a/book/pages/layout_demo.ipynb
+++ b/book/pages/layout_demo.ipynb
@@ -16,7 +16,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2024-02-02 10:32:11.133\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mgplugins.gmeep\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m39\u001b[0m - \u001b[1mMeep '1.28.0' installed at ['/home/parkeraa/miniconda3/envs/mp/lib/python3.11/site-packages/meep']\u001b[0m\n"
+      "\u001b[32m2024-02-02 13:15:11.847\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mgplugins.gmeep\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m39\u001b[0m - \u001b[1mMeep '1.28.0' installed at ['/home/parkeraa/miniconda3/envs/mp/lib/python3.11/site-packages/meep']\u001b[0m\n"
      ]
     }
    ],
@@ -40,10 +40,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2024-02-02 10:32:24.512\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mgdsfactory.technology.layer_views\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m790\u001b[0m - \u001b[1mImporting LayerViews from YAML file: '/home/parkeraa/miniconda3/envs/mp/lib/python3.11/site-packages/ubcpdk/layers.yaml'.\u001b[0m\n",
-      "\u001b[32m2024-02-02 10:32:24.572\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mubcpdk\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m30\u001b[0m - \u001b[1mFound UBCpdk '2.3.4' installed at PosixPath('/home/parkeraa/miniconda3/envs/mp/lib/python3.11/site-packages/ubcpdk')\u001b[0m\n",
-      "\u001b[32m2024-02-02 10:32:24.676\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mgdsfactory.technology.layer_views\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m790\u001b[0m - \u001b[1mImporting LayerViews from YAML file: '/home/parkeraa/miniconda3/envs/mp/lib/python3.11/site-packages/gdsfactory/generic_tech/layer_views.yaml'.\u001b[0m\n",
-      "\u001b[32m2024-02-02 10:32:24.684\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mgdsfactory.pdk\u001b[0m:\u001b[36mactivate\u001b[0m:\u001b[36m307\u001b[0m - \u001b[1m'ubcpdk' PDK is now active\u001b[0m\n"
+      "\u001b[32m2024-02-02 13:15:13.721\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mgdsfactory.technology.layer_views\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m790\u001b[0m - \u001b[1mImporting LayerViews from YAML file: '/home/parkeraa/miniconda3/envs/mp/lib/python3.11/site-packages/ubcpdk/layers.yaml'.\u001b[0m\n",
+      "\u001b[32m2024-02-02 13:15:13.755\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mubcpdk\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m30\u001b[0m - \u001b[1mFound UBCpdk '2.3.4' installed at PosixPath('/home/parkeraa/miniconda3/envs/mp/lib/python3.11/site-packages/ubcpdk')\u001b[0m\n",
+      "\u001b[32m2024-02-02 13:15:13.831\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mgdsfactory.technology.layer_views\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m790\u001b[0m - \u001b[1mImporting LayerViews from YAML file: '/home/parkeraa/miniconda3/envs/mp/lib/python3.11/site-packages/gdsfactory/generic_tech/layer_views.yaml'.\u001b[0m\n",
+      "\u001b[32m2024-02-02 13:15:13.834\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mgdsfactory.pdk\u001b[0m:\u001b[36mactivate\u001b[0m:\u001b[36m307\u001b[0m - \u001b[1m'ubcpdk' PDK is now active\u001b[0m\n"
      ]
     }
    ],
@@ -55,17 +55,6 @@
     "gf.CONF.display_type = \"klayout\""
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mzi = uc.mzi(delta_length=80, length_x=1, length_y=20)\n",
-    "TE_MZI = uc.add_fiber_array(component=mzi, component_name='MyCoolMZI1')\n",
-    "TE_MZI.plot()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -74,7 +63,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "50cfbbf14f3e430b91b5d2b7533e0146",
+       "model_id": "8b2e48a4dcee4601bd80e0a1f190f960",
        "version_major": 2,
        "version_minor": 0
       },
@@ -83,7 +72,7 @@
        "\n",
        "            <div style=\"display: inline-block;\">\n",
        "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
-       "                    Figure 1\n",
+       "                    Figure\n",
        "                </div>\n",
        "                <img 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' width=800.0/>\n",
        "            </div>\n",
@@ -98,18 +87,21 @@
     }
    ],
    "source": [
+    "mzi = uc.mzi(delta_length=80, length_x=1, length_y=20)\n",
+    "TE_MZI = uc.add_fiber_array(component=mzi, component_name='MyCoolMZI1')\n",
+    "TE_MZI.plot()\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1204ed654c1a4cedaf7ce90cca0cb96d",
+       "model_id": "f07eafc3dca84b239f5aae4df1956604",
        "version_major": 2,
        "version_minor": 0
       },
@@ -141,13 +133,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7a34146913684cd3b044a559cc630d9e",
+       "model_id": "d30c7d7790d04cce824a60287faf2419",
        "version_major": 2,
        "version_minor": 0
       },
@@ -179,13 +171,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "12db2040268b43eaab6ea30af877fbd3",
+       "model_id": "6b252f09ad3346d38cfde09d9f38971d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -217,13 +209,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "33ab87f4aa2c418da250ee3c1ad30b46",
+       "model_id": "a810c829f6504e4fba1393b028949263",
        "version_major": 2,
        "version_minor": 0
       },
@@ -260,13 +252,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6e0ac848acc8446e9458634dc8a4bbd1",
+       "model_id": "ca85f76103f34abb80eb16f170be6585",
        "version_major": 2,
        "version_minor": 0
       },
@@ -301,13 +293,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e08a6ad782384b519021bf77ccec36e5",
+       "model_id": "188ee64eef324bf3b32edfa0ddd0c45e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -340,13 +332,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "46a644f109534ed091a8e2bea5c9c46b",
+       "model_id": "c8614991a4d74663aa3579c142f67996",
        "version_major": 2,
        "version_minor": 0
       },
@@ -386,22 +378,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.close('all')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "389052f08f384a1094dda3033ac831bd",
+       "model_id": "62930a1de22b43949df24b32ecfe8453",
        "version_major": 2,
        "version_minor": 0
       },
@@ -441,13 +424,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2bbf857e83c6408f87bfe0f02a809a0e",
+       "model_id": "abe591e45d454a8e80efe48d52c9f24a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -484,13 +467,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2eb86cfa4a804006bc2d25a2c5112498",
+       "model_id": "62e0c59c447847babf1ba75879fc18ea",
        "version_major": 2,
        "version_minor": 0
       },
@@ -527,13 +510,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "18aae99550c34f57ae146e49fcff1f00",
+       "model_id": "c43f589174384875a57eb8f0a098d017",
        "version_major": 2,
        "version_minor": 0
       },
@@ -589,13 +572,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4f44e3c186ed4ed08acc74197118818f",
+       "model_id": "0f87bc435ba241f79ac03c77cd9228f9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -648,24 +631,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "my_device_fibers = uc.add_fiber_array(component=my_device.rotate(), component_name='MyCoolDevice1')\n",
-    "\n",
-    "my_device_fibers.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7ae22125a2a343ee8eb6bce3191dd97b",
+       "model_id": "6f1a6d1ed4944f1180d8b2dca0ca1382",
        "version_major": 2,
        "version_minor": 0
       },
@@ -674,7 +646,7 @@
        "\n",
        "            <div style=\"display: inline-block;\">\n",
        "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
-       "                    Figure 13\n",
+       "                    Figure\n",
        "                </div>\n",
        "                <img src='data:image/png;base64,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' width=800.0/>\n",
        "            </div>\n",
@@ -689,18 +661,21 @@
     }
    ],
    "source": [
+    "my_device_fibers = uc.add_fiber_array(component=my_device.rotate(), component_name='MyCoolDevice1')\n",
+    "\n",
+    "my_device_fibers.plot()\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5b4806204273406b80521ea9842a39a4",
+       "model_id": "161a93e22f4d45499f273149f32c0449",
        "version_major": 2,
        "version_minor": 0
       },
@@ -742,13 +717,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "af85d7207f7842feadc72b9bc6486218",
+       "model_id": "964040733c224ae4982b7ff69ac56b2e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -784,13 +759,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3e6417510ff14f23937ad48fd4b8441e",
+       "model_id": "a7945998dd8f4a41895fe2c98e6e4489",
        "version_major": 2,
        "version_minor": 0
       },
@@ -831,32 +806,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.close('all')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dl2.move(origin=dl2.center, destination = dl2.center + (-250, -200))\n",
-    "my_submission.plot()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fe5a4c23639f48dfb32c8f3386862d4b",
+       "model_id": "59695ff5eee640a79603d0746fed773a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -865,7 +821,7 @@
        "\n",
        "            <div style=\"display: inline-block;\">\n",
        "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
-       "                    Figure 13\n",
+       "                    Figure\n",
        "                </div>\n",
        "                <img 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' width=800.0/>\n",
        "            </div>\n",
@@ -880,7 +836,9 @@
     }
    ],
    "source": [
-    "plt.show()"
+    "dl2.move(origin=dl2.center, destination = dl2.center + (-250, -200))\n",
+    "my_submission.plot()\n",
+    "plt.show()\n"
    ]
   },
   {