364 lines
14 KiB
Plaintext
364 lines
14 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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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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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from mosplot.plot import load_lookup_table, Mosfet, Expression\n",
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"import ipywidgets as widgets\n",
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"from ipywidgets import interactive\n",
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"from ipywidgets import interactive_output, HBox, VBox\n",
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"import matplotlib.ticker as ticker "
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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": 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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"source": [
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"lookup_table_nmos = load_lookup_table('../sg13_nmos_lv_LUT.npz')\n",
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"lookup_table_pmos = load_lookup_table('../sg13_pmos_lv_LUT.npz')"
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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": 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))"
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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": 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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"source_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"def plot_data_vs_data(x_values, y_values, z_values, length, x_axis_name, y_axis_name='y', y_multiplier=1, log=False):\n",
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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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" \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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"\n",
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" def update_plot(selected_length, x_value=None, y_value=None):\n",
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" plt.figure(figsize=(12, 8)) # Make the figure wider (adjust as needed)\n",
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"\n",
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" if selected_length == \"Show All\":\n",
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" mask = np.ones_like(length_flat, dtype=bool)\n",
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" else:\n",
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" selected_length_in_micro = float(selected_length.replace(' μm', ''))\n",
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" tolerance = 0.01 # Tighten the tolerance to avoid unwanted data points\n",
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" mask = np.abs(length_flat * 1e6 - selected_length_in_micro) < tolerance\n",
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"\n",
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" # Apply the mask to the data\n",
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" x_values_for_length = x_values_flat[mask]\n",
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" y_values_for_length = y_values_flat[mask] * y_multiplier\n",
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" z_values_for_length = z_values_flat[mask]\n",
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" length_for_length = length_flat[mask] * 1e6\n",
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"\n",
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" if selected_length == \"Show All\":\n",
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" for length_value in np.unique(length_for_length):\n",
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" mask_all = (length_for_length == length_value)\n",
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" plt.plot(x_values_for_length[mask_all], y_values_for_length[mask_all])\n",
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"\n",
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" min_length = np.min(unique_lengths_in_micro)\n",
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" max_length = np.max(unique_lengths_in_micro)\n",
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" plt.title(f'{y_axis_name} vs {x_axis_name} (Length from {min_length:.2f} μm to {max_length:.2f} μm)')\n",
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"\n",
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" else:\n",
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" plt.plot(x_values_for_length, y_values_for_length)\n",
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" plt.title(f'{y_axis_name} vs {x_axis_name} for {selected_length}')\n",
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"\n",
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" plt.xlabel(f'{x_axis_name}')\n",
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" plt.ylabel(f'{y_axis_name}')\n",
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"\n",
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" if log:\n",
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" plt.yscale('log')\n",
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" plt.gca().yaxis.set_major_locator(ticker.LogLocator(base=10, subs=[], numticks=10))\n",
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" plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f'$10^{int(np.log10(x))}$'))\n",
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" plt.ylabel(f'{y_axis_name} (Log Base 10)')\n",
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"\n",
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" if y_value is not None and x_value_widget.disabled:\n",
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" closest_index = np.abs(y_values_for_length - y_value).argmin()\n",
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" closest_x = x_values_for_length[closest_index]\n",
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" closest_y = y_values_for_length[closest_index]\n",
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" corresponding_z = z_values_for_length[closest_index]\n",
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"\n",
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" plt.scatter(closest_x, closest_y, color='blue', label=f'Point ({closest_x:.2f}, {closest_y:.2f})')\n",
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" z_value_widget.value = corresponding_z\n",
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" print(f\"The corresponding {x_axis_name} value for {y_axis_name} = {closest_y:.2f} is: {closest_x:.2f}\")\n",
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" elif x_value is not None and y_value_widget.disabled:\n",
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" closest_index = np.abs(x_values_for_length - x_value).argmin()\n",
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" closest_x = x_values_for_length[closest_index]\n",
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" closest_y = y_values_for_length[closest_index]\n",
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" corresponding_z = z_values_for_length[closest_index]\n",
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"\n",
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" plt.scatter(closest_x, closest_y, color='red', label=f'Point ({closest_x:.2f}, {closest_y:.2f})')\n",
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" z_value_widget.value = corresponding_z\n",
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" print(f\"The corresponding {y_axis_name} value for {x_axis_name} = {closest_x:.2f} is: {closest_y:.2f}\")\n",
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"\n",
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" plt.grid(True)\n",
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" plt.legend()\n",
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" plt.show()\n",
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"\n",
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" dropdown_options = [\"Show All\"] + [f'{length:.2f} μm' for length in unique_lengths_in_micro]\n",
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" length_widget = widgets.Dropdown(\n",
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" options=dropdown_options,\n",
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" value=dropdown_options[0],\n",
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" description='Length:',\n",
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" layout=widgets.Layout(width='500px') # Make the dropdown wider\n",
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" )\n",
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"\n",
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" x_value_widget = widgets.FloatText(\n",
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" value=np.mean(x_values_flat),\n",
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" description=f\"{x_axis_name}:\",\n",
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" disabled=False,\n",
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" layout=widgets.Layout(width='300px', margin='0 40px 0 0'), # Push input boxes more to the right\n",
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" description_width='150px' # Smaller description width\n",
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" )\n",
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"\n",
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" y_value_widget = widgets.FloatText(\n",
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" value=None,\n",
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" description=f\"{y_axis_name}:\",\n",
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" disabled=True,\n",
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" layout=widgets.Layout(width='300px', margin='0 40px 0 0'), # Push input boxes more to the right\n",
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" description_width='150px' # Smaller description width\n",
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" )\n",
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"\n",
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" z_value_widget = widgets.FloatText(\n",
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" value=None,\n",
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" description=f\" Vgs:\",\n",
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" disabled=True,\n",
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" layout=widgets.Layout(width='300px', margin='0 40px 0 0'), # Push input boxes more to the right\n",
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" description_width='150px' # Smaller description width\n",
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" )\n",
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"\n",
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" select_x_or_y_widget = widgets.Checkbox(\n",
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" value=True,\n",
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" description=f\"{x_axis_name} (uncheck for {y_axis_name})\",\n",
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" layout=widgets.Layout(width='300px') # Make the checkbox wider\n",
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" )\n",
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"\n",
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" def toggle_x_or_y(change):\n",
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" if change['new']:\n",
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" x_value_widget.disabled = False\n",
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" y_value_widget.disabled = True\n",
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" else:\n",
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" x_value_widget.disabled = True\n",
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" y_value_widget.disabled = False\n",
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"\n",
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" select_x_or_y_widget.observe(toggle_x_or_y, names='value')\n",
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"\n",
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" output = interactive_output(update_plot, {\n",
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" 'selected_length': length_widget,\n",
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" 'x_value': x_value_widget,\n",
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" 'y_value': y_value_widget\n",
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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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" if ro_value < 1e3:\n",
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" return ro_value, \"Ω\"\n",
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" elif ro_value < 1e6:\n",
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" return ro_value / 1e3, \"kΩ\"\n",
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" elif ro_value < 1e9:\n",
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" return ro_value / 1e6, \"MΩ\"\n",
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" else:\n",
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" return ro_value / 1e9, \"GΩ\"\n",
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"\n",
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"def display_current(Id_value):\n",
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" \"\"\"Determine the current value and its unit.\"\"\"\n",
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" if Id_value < 1e-6:\n",
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" return Id_value * 1e9, \"nA\" # Convert to nA\n",
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" elif Id_value < 1e-3:\n",
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" return Id_value * 1e6, \"μA\" # Convert to μA\n",
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" else:\n",
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" return Id_value * 1e3, \"mA\" # Convert to mA\n",
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" \n",
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"def dB_to_linear(av_db):\n",
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" return 10 ** (av_db / 20)\n",
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"\n",
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"\n",
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"def determine_inversion_region(gm_id_value, device_type):\n",
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" \"\"\"Determine the inversion region based on gm/id value for NMOS or PMOS.\"\"\"\n",
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" if device_type == 'nmos':\n",
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" if gm_id_value > 20:\n",
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" return \"Weak Inversion\"\n",
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" elif 10 < gm_id_value <= 20:\n",
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" return \"Moderate Inversion\"\n",
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" else:\n",
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" return \"Strong Inversion\"\n",
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" elif device_type == 'pmos':\n",
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" if gm_id_value > 20:\n",
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" return \"Weak Inversion\"\n",
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" elif 10 < gm_id_value <= 20:\n",
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" return \"Moderate Inversion\"\n",
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" else:\n",
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" return \"Strong Inversion\"\n",
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" else:\n",
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" raise ValueError(\"Invalid device type. Use 'nmos' or 'pmos'.\")\n",
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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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"metadata": {},
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"source": [
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"# NMOS GMID"
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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": 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": "c0d716a395d84be68ee1dbc73a913175",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(Dropdown(description='Length:', layout=Layout(width='500px'), options=('Show All', '0.13 μm', '…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"width_values = nmos.width\n",
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"id_values = nmos.extracted_table['id']\n",
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"gm_values = nmos.extracted_table['gm']\n",
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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(\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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"cell_type": "markdown",
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"id": "e847c359-b57e-4e84-b0dc-93616d575efd",
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"metadata": {},
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"source": [
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"# PMOS GMID"
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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": 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": "cba5a4c794944ea1900cd54144dad957",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(Dropdown(description='Length:', layout=Layout(width='500px'), options=('Show All', '0.13 μm', '…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"width_values = pmos.width\n",
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"id_values = pmos.extracted_table['id']\n",
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"gm_values = pmos.extracted_table['gm']\n",
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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, length_2d_pmos, 'gm/id', 'gm/gds')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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