push new flatten logic
This commit is contained in:
PhillipRambo
parent
07d52ddd14
commit
be5255ede9
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@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 34,
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"id": "9f325daf-eb3b-4cf7-8ffe-b9b94e7f66ea",
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"metadata": {},
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"outputs": [],
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@ -18,7 +18,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 35,
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"id": "b5b31aca-47bf-4461-8e50-16c20f03b337",
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"metadata": {},
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"outputs": [],
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@ -29,41 +29,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "a03cd944-2432-457c-9b88-486ab781fde6",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"dict_keys(['sg13_lv_nmos ', 'description', 'simulator', 'parameter_names', 'device_parameters'])\n"
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]
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}
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],
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"source": [
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"print(lookup_table_nmos.keys())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 36,
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"id": "743dc381-0d35-4aa9-847c-c42c80c17786",
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"metadata": {},
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"outputs": [],
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"source": [
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"nmos = Mosfet(lookup_table=lookup_table_nmos, mos=\"sg13_lv_nmos \", vbs=0.0, vds=0.6)\n",
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"pmos = Mosfet(lookup_table=lookup_table_pmos, mos=\"sg13_lv_pmos\", vbs=0.0, vds=-0.6, vgs=(-1.2, -0.15))\n",
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"\n",
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"rows_0, cols_0 = np.shape(nmos.extracted_table['gm']) # just for getting the shape of the data\n",
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"rows_1, cols_1 = np.shape(pmos.extracted_table['gm']) # just for getting the shape of the data\n",
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"reshaped_lengths_nmos = np.tile(nmos.length[:, np.newaxis], (1, cols_0))\n",
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"reshaped_lengths_pmos = np.tile(pmos.length[:, np.newaxis], (1, cols_1))"
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"pmos = Mosfet(lookup_table=lookup_table_pmos, mos=\"sg13_lv_pmos\", vbs=0.0, vds=-0.6, vgs=(-1.2, -0.15))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 37,
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"id": "b27d5fca-3436-4df7-895f-f6a4bbd7a80d",
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"metadata": {
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"jupyter": {
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@ -76,11 +53,11 @@
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" x_values_flat = np.array(x_values).flatten()\n",
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" y_values_flat = np.array(y_values, dtype=np.float64).flatten()\n",
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" z_values_flat = np.array(z_values, dtype=np.float64).flatten()\n",
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" length_flat = np.array(length).flatten()\n",
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"\n",
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" # Ensure all inputs have the same length\n",
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" if not (len(x_values_flat) == len(y_values_flat) == len(z_values_flat) == len(length_flat)):\n",
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" raise ValueError(\"All input arrays (x_values, y_values, z_values, length) must have the same number of elements.\")\n",
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" \n",
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" length_arr = np.array(length)\n",
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" \n",
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" length_flat = length_arr.flatten()\n",
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" \n",
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"\n",
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" unique_lengths = np.unique(length_flat)\n",
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" unique_lengths_in_micro = unique_lengths * 1e6\n",
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@ -201,6 +178,23 @@
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" })\n",
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"\n",
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" display(VBox([length_widget, select_x_or_y_widget, HBox([x_value_widget, y_value_widget]), z_value_widget, output]))\n",
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"\n",
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"\n",
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"\n",
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"def tile_length_to_match_data(length_array, data_array):\n",
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" length_array = np.array(length_array).flatten() \n",
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" data_shape = data_array.shape \n",
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" \n",
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" if length_array.size == data_shape[0]:\n",
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" # length matches number of rows, repeat along columns\n",
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" return np.tile(length_array.reshape(-1, 1), (1, data_shape[1]))\n",
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" elif length_array.size == data_shape[1]:\n",
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" # length matches number of columns, repeat along rows\n",
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" return np.tile(length_array.reshape(1, -1), (data_shape[0], 1))\n",
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" else:\n",
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" raise ValueError(f\"Length array size {length_array.size} does not match any dimension of data shape {data_shape}\")\n",
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"\n",
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"\n",
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" \n",
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"def display_resistance(ro_value):\n",
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" \"\"\"Determine the resistance value and its unit.\"\"\"\n",
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@ -247,6 +241,18 @@
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" \n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"id": "fc95a0ff-ba36-46ce-9fa1-64d6d2d7770f",
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"metadata": {},
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"outputs": [],
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"source": [
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"### ------ automated flattening based on shape ------### \n",
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"length_2d_pmos = tile_length_to_match_data(pmos.length, pmos.extracted_table['gm'])\n",
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"length_2d_nmos = tile_length_to_match_data(nmos.length, nmos.extracted_table['gm'])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2fc675aa-6d59-4d74-83e2-18c56353db0d",
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@ -257,14 +263,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 39,
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"id": "b7cc630f-b385-47a6-a6f9-ac0d10effffe",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "b5ed71dbc38a4a55b7b1717f2b0ca7d2",
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"model_id": "c0d716a395d84be68ee1dbc73a913175",
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"version_major": 2,
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"version_minor": 0
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},
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@ -283,7 +289,14 @@
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"gds_values = nmos.extracted_table['gds']\n",
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"vgs_values= nmos.extracted_table['vgs']\n",
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"\n",
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"plot_data_vs_data(gm_values/id_values, gm_values/gds_values, vgs_values, reshaped_lengths_nmos, 'gm/id', 'gm/gds')"
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"plot_data_vs_data(\n",
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" gm_values/id_values,\n",
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" gm_values/gds_values,\n",
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" vgs_values,\n",
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" length_2d_nmos,\n",
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" 'gm/id',\n",
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" 'gm/gds'\n",
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")"
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]
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},
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{
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@ -296,14 +309,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 111,
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"execution_count": 40,
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"id": "3727c42d-a4bf-4eb0-bc11-6e859ae41324",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "25e14d69e1084f71a4f21a94fe991a02",
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"model_id": "cba5a4c794944ea1900cd54144dad957",
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"version_major": 2,
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"version_minor": 0
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},
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@ -322,7 +335,7 @@
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"gds_values = pmos.extracted_table['gds']\n",
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"vgs_values= pmos.extracted_table['vgs']\n",
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"\n",
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"plot_data_vs_data(gm_values/id_values, gm_values/gds_values, vgs_values, reshaped_lengths_pmos, 'gm/id', 'gm/gds')"
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"plot_data_vs_data(gm_values/id_values, gm_values/gds_values, vgs_values, length_2d_pmos, 'gm/id', 'gm/gds')"
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]
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}
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],
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@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -17,7 +17,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -27,7 +27,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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@ -44,22 +44,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"nmos = Mosfet(lookup_table=lookup_table_nmos, mos=\"sg13_lv_nmos \", vbs=0.0, vds=0.6)\n",
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"pmos = Mosfet(lookup_table=lookup_table_pmos, mos=\"sg13_lv_pmos\", vbs=0.0, vds=-0.6, vgs=(-1.2, -0.15))\n",
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"\n",
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"rows_0, cols_0 = np.shape(nmos.extracted_table['gm']) # just for getting the shape of the data\n",
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"rows_1, cols_1 = np.shape(pmos.extracted_table['gm']) # just for getting the shape of the data\n",
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"reshaped_lengths_nmos = np.tile(nmos.length[:, np.newaxis], (1, cols_0))\n",
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"reshaped_lengths_pmos = np.tile(pmos.length[:, np.newaxis], (1, cols_1))"
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"pmos = Mosfet(lookup_table=lookup_table_pmos, mos=\"sg13_lv_pmos\", vbs=0.0, vds=-0.6, vgs=(-1.2, -0.15))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 5,
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"metadata": {
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"jupyter": {
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"source_hidden": true
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@ -71,11 +66,11 @@
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" x_values_flat = np.array(x_values).flatten()\n",
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" y_values_flat = np.array(y_values, dtype=np.float64).flatten()\n",
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" z_values_flat = np.array(z_values, dtype=np.float64).flatten()\n",
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" length_flat = np.array(length).flatten()\n",
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"\n",
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" # Ensure all inputs have the same length\n",
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" if not (len(x_values_flat) == len(y_values_flat) == len(z_values_flat) == len(length_flat)):\n",
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" raise ValueError(\"All input arrays (x_values, y_values, z_values, length) must have the same number of elements.\")\n",
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" \n",
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" length_arr = np.array(length)\n",
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" \n",
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" length_flat = length_arr.flatten()\n",
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" \n",
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"\n",
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" unique_lengths = np.unique(length_flat)\n",
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" unique_lengths_in_micro = unique_lengths * 1e6\n",
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@ -198,6 +193,22 @@
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" display(VBox([length_widget, select_x_or_y_widget, HBox([x_value_widget, y_value_widget]), z_value_widget, output]))\n",
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"\n",
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"\n",
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"\n",
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"def tile_length_to_match_data(length_array, data_array):\n",
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" length_array = np.array(length_array).flatten() \n",
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" data_shape = data_array.shape \n",
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" \n",
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" if length_array.size == data_shape[0]:\n",
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" # length matches number of rows, repeat along columns\n",
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" return np.tile(length_array.reshape(-1, 1), (1, data_shape[1]))\n",
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" elif length_array.size == data_shape[1]:\n",
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" # length matches number of columns, repeat along rows\n",
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" return np.tile(length_array.reshape(1, -1), (data_shape[0], 1))\n",
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" else:\n",
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" raise ValueError(f\"Length array size {length_array.size} does not match any dimension of data shape {data_shape}\")\n",
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"\n",
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"\n",
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" \n",
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"def display_resistance(ro_value):\n",
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" \"\"\"Determine the resistance value and its unit.\"\"\"\n",
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" if ro_value < 1e3:\n",
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@ -243,6 +254,17 @@
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" \n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"### ------ automated flattening based on shape ------### \n",
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"reshaped_lengths_pmos = tile_length_to_match_data(pmos.length, pmos.extracted_table['gm'])\n",
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"reshaped_lengths_nmos = tile_length_to_match_data(nmos.length, nmos.extracted_table['gm'])"
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]
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},
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{
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"attachments": {
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"image.png": {
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "ea4f549168444a6da3039c78cb0ef5f0",
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"model_id": "72114aa1708645529ec782f5479a3a60",
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"version_major": 2,
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"version_minor": 0
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},
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],
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"source": [
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"nmos_M6 = Mosfet(lookup_table=lookup_table_nmos, mos=\"sg13_lv_nmos \", vbs=0, vds=0.6)\n",
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"rows_0, cols_0 = np.shape(nmos_M6.extracted_table['gm'])\n",
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"reshaped_lengths_nmos_M6 = np.tile(nmos_M6.length[:, np.newaxis], (1, cols_0))\n",
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"reshaped_lengths_nmos_M6 = tile_length_to_match_data(nmos_M6.length, nmos_M6.extracted_table['gm'])\n",
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"\n",
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"width_values_M6 = nmos_M6.width\n",
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"id_values_M6 = nmos_M6.extracted_table['id']\n",
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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@ -331,7 +352,7 @@
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"7.949913192125772e-05"
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]
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},
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"execution_count": 13,
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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@ -371,13 +392,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "c13371af34b44b4fbcd6d05b532d361f",
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"model_id": "d2c6f85fa97b4d9d8844e236d1b23f3f",
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"version_major": 2,
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"version_minor": 0
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},
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@ -391,8 +412,7 @@
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],
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"source": [
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"pmos_M7 = Mosfet(lookup_table=lookup_table_pmos, mos=\"sg13_lv_pmos\", vbs=0, vds=-0.6, vgs=(-1.2, -0.1))\n",
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"rows_0, cols_0 = np.shape(pmos_M7.extracted_table['gm'])\n",
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"reshaped_lengths_pmos_M7 = np.tile(pmos_M7.length[:, np.newaxis], (1, cols_0))\n",
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"reshaped_lengths_pmos_M7 = tile_length_to_match_data(pmos_M7.length, pmos_M7.extracted_table['gm'])\n",
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"\n",
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"width_values_M7 = pmos_M7.width\n",
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"id_values_M7 = pmos_M7.extracted_table['id']\n",
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},
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{
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"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -418,7 +438,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -441,13 +461,13 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "2eca17df295841629e74fb72e1cff63f",
|
||||
"model_id": "3423adf060a5431c9ed9dcfd25cdd5cb",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -461,7 +481,7 @@
|
|||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "88996c8fb302472b8fb5add0523f26dc",
|
||||
"model_id": "d9a07198c63c44209996a193ba18660b",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -480,7 +500,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -550,7 +570,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 43,
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -559,7 +579,7 @@
|
|||
"2.0004294796936965e-06"
|
||||
]
|
||||
},
|
||||
"execution_count": 43,
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -577,7 +597,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 44,
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -597,7 +617,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 45,
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -625,13 +645,13 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 47,
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "5d7b576ad312473eaeab532b93f3e92f",
|
||||
"model_id": "bc344d417b284a71abea7fb90081872e",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -645,7 +665,7 @@
|
|||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "e116eacdd44e4571b3f183da0118a4fa",
|
||||
"model_id": "b312811f418442868110cc154d1cd4b7",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -660,11 +680,8 @@
|
|||
"source": [
|
||||
"pmos_M12 = Mosfet(lookup_table=lookup_table_pmos, mos=\"sg13_lv_pmos\", vbs=-0.2, vds=-0.6, vgs=(-1.2, -0.1))\n",
|
||||
"nmos_M34 = Mosfet(lookup_table=lookup_table_nmos, mos=\"sg13_lv_nmos \", vbs=0, vds=0.6)\n",
|
||||
"rows_0, cols_0 = np.shape(nmos_M34.extracted_table['gm']) # just for getting the shape of the data\n",
|
||||
"rows_1, cols_1 = np.shape(pmos_M12.extracted_table['gm']) # just for getting the shape of the data\n",
|
||||
"reshaped_lengths_nmos_M34 = np.tile(nmos_M34.length[:, np.newaxis], (1, cols_0))\n",
|
||||
"reshaped_lengths_pmos_M12 = np.tile(pmos_M12.length[:, np.newaxis], (1, cols_1))\n",
|
||||
"\n",
|
||||
"reshaped_lengths_nmos_M34 = tile_length_to_match_data(nmos_M34.length, nmos_M34.extracted_table['gm'])\n",
|
||||
"reshaped_lengths_pmos_M12 = tile_length_to_match_data(pmos_M12.length, pmos_M12.extracted_table['gm'])\n",
|
||||
"\n",
|
||||
"id_values_M12 = pmos_M12.extracted_table['id']\n",
|
||||
"gm_values_M12 = pmos_M12.extracted_table['gm']\n",
|
||||
|
|
@ -682,7 +699,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 48,
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -709,13 +726,13 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "4cca567128c14cb68100041e1a57c919",
|
||||
"model_id": "e503a959702844d09e52f5cc34072b5b",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -729,7 +746,7 @@
|
|||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "6c92cedcecfb47f491e2b860b0c65a31",
|
||||
"model_id": "9299f09d02b1408b88c4ad86a163ce34",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -751,7 +768,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -804,7 +821,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 52,
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -906,13 +923,6 @@
|
|||
"\"\"\"\n",
|
||||
"print(summary)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
|
|||
Loading…
Reference in New Issue