MOSFET-1.ipynb

{
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   "cell_type": "markdown",
   "id": "e79f2fb6-a5a6-4bd6-8b9e-6f3344cd729e",
   "metadata": {},
   "source": [
    "# Digital Twin with Python\n",
    "\n",
    "### Dr. Tirthajyoti Sarkar\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c86c66db-8cc5-424f-bc9d-7e79e920dea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf, numpy as np, matplotlib.pyplot as plt\n",
    "from nn import *\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "31addb0d-bd18-4450-91bb-995b3a74315d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sub_vth(w_l,vgs,vth,temp=300):\n",
    "    \"\"\"\n",
    "    Helper function to calculate sub-vth current analytically.\n",
    "    \n",
    "    Uses randomized parameters to mimic \n",
    "    measurement noise and manufacturing/material variability\n",
    "    \"\"\"\n",
    "    # Electron charge\n",
    "    q = 1.60218e-19\n",
    "    # Boltzman constant\n",
    "    k = 1.3806e-23\n",
    "    # Capacitance factor (randomized to mimic manufacturing variability)\n",
    "    eta = 1.2+0.01*np.random.normal()\n",
    "    # Mobility factor/coefficient (randomized to mimic material and manufacturing variability)\n",
    "    w_l = w_l*(1+0.01*np.random.normal())\n",
    "    \n",
    "    return w_l*np.exp(q*(vgs-vth)/(eta*k*temp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "353f91d2-cbd4-425b-8e8c-9990c8d9e277",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MOSFET:\n",
    "    def __init__(self,params=None,terminals=None):\n",
    "        \n",
    "        # Params\n",
    "        if params is None:\n",
    "            self._params_ = {'BV':20,\n",
    "                             'Vth':1.0,\n",
    "                             'gm':1e-2}\n",
    "        else:\n",
    "            self._params_ = params\n",
    "        \n",
    "        # Terminals\n",
    "        if terminals is None:\n",
    "            self._terminals_ = {'source':0.0,\n",
    "                        'drain':0.0,\n",
    "                        'gate':0.0}\n",
    "        else:\n",
    "            self._terminals_ = terminals\n",
    "        \n",
    "        # Determine state\n",
    "        self._state_ = self.determine_state()\n",
    "        \n",
    "        # Leakage model trained?\n",
    "        self._leakage_ = False\n",
    "        self.leakage_model = None\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return \"Digital Twin of a MOSFET\"\n",
    "    \n",
    "    def determine_state(self,vgs=None):\n",
    "        \"\"\"\n",
    "        \"\"\"\n",
    "        if vgs is None:\n",
    "            vgs = self._terminals_['gate'] - self._terminals_['source']\n",
    "        else:\n",
    "            vgs = vgs\n",
    "        if vgs > self._params_['Vth']:\n",
    "            return 'ON'\n",
    "        else:\n",
    "            return 'OFF'\n",
    "    \n",
    "    def id_vd(self,vgs=None,vds=None,rounding=True):\n",
    "        \"\"\"\n",
    "        Calculates drain-source current from terminal voltages and gm \n",
    "        \"\"\"        \n",
    "        if vds is None:\n",
    "            vds = self._terminals_['drain'] - self._terminals_['source']\n",
    "        else:\n",
    "            vds = vds\n",
    "        if vgs is None:\n",
    "            vgs = self._terminals_['gate'] - self._terminals_['source']\n",
    "        else:\n",
    "            vgs = vgs\n",
    "        \n",
    "        vth = self._params_['Vth']\n",
    "        state = self.determine_state(vgs=vgs)\n",
    "        self._state_ = state\n",
    "        \n",
    "        if state=='ON':\n",
    "            if vds <= vgs - vth:\n",
    "                ids = self._params_['gm']*(vgs - vth - (vds/2))*vds\n",
    "            else:\n",
    "                ids = (self._params_['gm']/2)*(vgs-vth)**2\n",
    "            if rounding:\n",
    "                return round(ids,3)\n",
    "            else:\n",
    "                return ids\n",
    "        else:\n",
    "            return sub_vth(w_l=self._params_['gm'],\n",
    "                           vgs=vgs,\n",
    "                           vth=vth)\n",
    "            #return 0.0\n",
    "        \n",
    "    def rdson(self,vgs=None,vds=None):\n",
    "        \"\"\"\n",
    "        Calculates Rdson i.e. on-state resistance of the MOSFET\n",
    "        \"\"\"\n",
    "        if vds is None:\n",
    "            vds = self._terminals_['drain'] - self._terminals_['source']\n",
    "        else:\n",
    "            vds = vds\n",
    "        if vgs is None:\n",
    "            vgs = self._terminals_['gate'] - self._terminals_['source']\n",
    "        else:\n",
    "            vgs = vgs\n",
    "        \n",
    "        ids = self.id_vd(vgs=vgs,vds=vds,rounding=False)\n",
    "        vds_delta = vds+0.001\n",
    "        ids_delta = self.id_vd(vgs=vgs,vds=vds_delta,rounding=False)\n",
    "        rdson = 0.001/(ids_delta-ids)\n",
    "        \n",
    "        return round(rdson,3)\n",
    "        \n",
    "    def train_leakage(self,data=None,\n",
    "                      batch_size=5,\n",
    "                      epochs=20,\n",
    "                      learning_rate=2e-5,\n",
    "                      verbose=1):\n",
    "        \"\"\"\n",
    "        Trains the digital twin for leakage current model with experimental data\n",
    "        Args:\n",
    "            data: The training data, expected as a Pandas DataFrame\n",
    "            batch_size (int): Training batch size\n",
    "            epochs (int): Number of epochs for training\n",
    "            learning_rate (float): Learning rate for training\n",
    "            verbose (0 or 1): Verbosity of display while training\n",
    "        \"\"\"\n",
    "        if data is None:\n",
    "            return \"No data to train with\"\n",
    "        X_train_scaled, X_test_scaled, \\\n",
    "        y_train_scaled, y_test_scaled = prepare_data(data,\n",
    "                                             input_cols=['w_l','vgs','vth'],\n",
    "                                             output_var='log-leakage',\n",
    "                                                     scaley=False)\n",
    "        # Deep-learning model\n",
    "        model = build_model(num_layers=3,\n",
    "                            architecture=[32,32,32],\n",
    "                            input_dim=3)\n",
    "        # Compile and train\n",
    "        model_trained = compile_train_model(model,\n",
    "                                            X_train_scaled,\n",
    "                                            y_train_scaled,\n",
    "                                            batch_size=batch_size,\n",
    "                                            epochs=epochs,\n",
    "                                            learning_rate=learning_rate,\n",
    "                                            verbose=verbose)\n",
    "        self.leakage_model = model_trained\n",
    "        self._leakage_ = True\n",
    "    \n",
    "    def leakage(self,\n",
    "                w_l=1e-2,\n",
    "                vgs=None,\n",
    "                vth=None):\n",
    "        \"\"\"\n",
    "        Calculates leakage current using the deep learning model\n",
    "        \"\"\"\n",
    "        if not self._leakage_:\n",
    "            return \"Leakage model is not trained yet\"\n",
    "        # Vgs\n",
    "        if vgs is None:\n",
    "            vgs = self._terminals_['gate'] - self._terminals_['source']\n",
    "        else:\n",
    "            vgs = vgs\n",
    "        # Vth\n",
    "        if vth is None:\n",
    "            vth = self._params_['Vth']\n",
    "        else:\n",
    "            vth = vth\n",
    "        # Predict\n",
    "        x = np.array([w_l,vgs,vth])\n",
    "        ip = x.reshape(-1,3)\n",
    "        result = float(10**(-self.leakage_model.predict(ip)))\n",
    "        \n",
    "        return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba120367-d77a-4976-aa9e-a37136b4d1af",
   "metadata": {},
   "source": [
    "## A `MOSFET` object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5ddd78a7-1326-4a2d-aaac-35346ff85f3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "mosfet = MOSFET()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "938541a1-4cbe-4e28-bb9a-e95c95a7571b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Digital Twin of a MOSFET"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mosfet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d5289b4e-e3b2-4ee5-837f-ccc0aae2aea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'OFF'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mosfet._state_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7d0d5b42-1372-43d0-9e04-f88f15d5ce91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BV': 20, 'Vth': 1.0, 'gm': 0.01}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mosfet._params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "330b4dea-a96c-4057-8ff7-0c0aebfa56d9",
   "metadata": {},
   "source": [
    "## Object state changes as terminal voltage assignment varies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "82b2500a-57b5-4816-bb92-9f87ac24a663",
   "metadata": {},
   "outputs": [],
   "source": [
    "mosfet = MOSFET(terminals={'source':0.0,\n",
    "            'drain':0.0,\n",
    "            'gate':2.0}, \n",
    "           params={'BV':20,\n",
    "                   'Vth':1.0,\n",
    "                   'gm':1e-2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "30d4dde2-9653-4007-a746-39a05b0cc5fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ON'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mosfet._state_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7d874141-5ef5-49d4-b23d-204fe4eecbf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "mosfet = MOSFET(terminals={'source':0.0,\n",
    "            'drain':0.0,\n",
    "            'gate':0.7},\n",
    "            params={'BV':20,\n",
    "                   'Vth':1.0,\n",
    "                   'gm':1e-2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a8a0ac93-370e-4262-8701-c5c7fc0803f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'OFF'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mosfet._state_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e127c407-15ac-4b0e-92cd-8de948d001da",
   "metadata": {},
   "source": [
    "## Id-Vd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b1916766-e8d4-479e-8293-c1f373e79d45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.631306143537147e-14"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.id_vd(vgs=0.2,vds=2.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0a9251ef-df26-414e-aeb5-c06f7e71ecab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.009"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.id_vd(vgs=3.0,vds=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "37011527-c16b-41c2-910d-bf747db26a65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.015"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.id_vd(vgs=3.0,vds=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d65a680e-606e-4139-8531-32885031837e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.id_vd(vgs=3.0,vds=3.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e31cd2aa-ee63-43b7-b209-f484c52d4cae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.id_vd(vgs=3.0,vds=4.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "274e3726-4d2f-4a60-9af7-a1b55248d386",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ON'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m._state_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "495ce6c1-7eaf-4ee6-86be-9e6b6aeae709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66.689"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.rdson(vgs=3.0,vds=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d48f7296-08f1-471f-9503-570c9a582a99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71.454"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.rdson(vgs=3.0,vds=0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "094d1a06-bad8-45a0-a235-07c349651aac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.008"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.rdson(vgs=5.0,vds=1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "af6d9861-f7dc-455b-bf84-f3bd7457c4a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ids = []\n",
    "mosfet = MOSFET()\n",
    "vds_vals = [0.01*i for i in range(1,501)]\n",
    "for v in vds_vals:\n",
    "    ids.append(mosfet.id_vd(vgs=3.0,vds=v,rounding=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "971fce0d-59f6-43fa-92f0-9115cf240fa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7/i4RUQ/MBCYWuTwJWBAR67YzttOAlyLin81okztsYMV2Ptus1SxfV8sFv3qKLdvqqa4S1512AKMH9c46LDMz62QqMVGdClQD5+cKJNUA5wDTImJhWjZSUuGKj6nAgZIm5rXdg2QlfuH8z2aRNAHYC/hNiesDJFUXlHUFvgxsITmIwCxzW7bVc9Gvn2bp2mTq9uUn7MWh3ivVzMwyUHFzVCNimqQpwHclDQbmA2cBo4Bz86reTLJpf/5EzuuA84C7JF1FcjLVJcAyCk6LkvQhYL/0bVdgX0mXp+/vjIjnCkL7VPpaatj/JOBySVOBl0mG+k8DxgNfiYiljXx0szYXEXz9zlk8+eqbAEx+z86cfeiobIMyM7NOq+IS1dSZwLeBM4D+JNs+nRgRjzbUKCLWSToSuAa4nKRH+WHg4ogoHHr/GEkCnDMh/QJYlD4TeGuP1lOBpyOi1PZWM4HngdOBQSS9qDOAUyJiu3pzzVrLr6e9xm+nJ9Om9xvRj//68Hgv2jMzs8yo5YcvWdbSTf9nzZo1i3HjxmUdjlW4J15ZxSev/yfb6oNBfWr48xfex059uzfe0MzMrAlmz57N+PHjAcZHxOymtKnEOapm1sreWL+Zz93yNNvqg27VVfz09Pc4STUzs8w5UTXr5Orqgy/eOoPl65KzML5+0t68Z5f+GUdlZmbmRNWs07v2gXk8Nv8NAD68/zBOO2hkxhGZmZklnKiadWJ/m7eCax+cB8CYQb34zkf28eIpMzMrG05UzTqppWtq+eKtM4iAHl2r+cnp76FXTaVuBGJmZh2RE1WzTmhrXT1f+O3TrNywBYDvfGQ8u+/UJ+OozMzM3smJqlkndM39c3nilWRT/1MPHMFHD9g544jMzMzezYmqWSfz+Etv8JNHXgJgzyF9+MZJ3oPXzMzKkxNVs05k9cYtXPK7Z4mA7l2r+NFpE+jetTrrsMzMzIpyomrWSUQEX/njTJaurQXg8hP2Zuxgz0s1M7Py5UTVrJOY8uQi7p65FIBj9t6JT03yfqlmZlbenKiadQIvv7GBb/wpOVZ5cJ8arvzYvt4v1czMyp4TVbMObsu2ev7t1mfYuKUOgKtP2Y8BvbplHJWZmVnjnKiadXA/eGAuzy1aA8B5h+3KYbsNyjgiMzOzpnGiataBPfPam/zk4WQrqr2H9uXfj90j44jMzMyazomqWQdVu7WOL015lvqAbtVVXPOJ/anp4q2ozMyscjhRNeugrrr3RRas2ADAF4/ZjT2GeCsqMzOrLE5UzTqg6S+v4sa/vwzA/iP6cf5hozOOyMzMrPmcqJp1MBu3bOPSqcnpUzVdqrj6lP3oUu3/1M3MrPL4by+zDubKe+bw6sqNAFx67B6MGdQ744jMzMxaxomqWQfy+Pw3+OU/XgXgwFH9Oee9u2YckZmZWcs5UTXrIDZu2cZlf3gOgB5dq7nq4/tRXeXTp8zMrHI5UTXrIK65fy4LV20C4LIP7sEuO/bKOCIzM7Pt40TVrAN4btFqbnwsWeV/wMh+nHHIqGwDMjMzawVOVM0q3Na6ei77/UzqA7pWi//+2L4e8jczsw7BiapZhfvZ317mhSVrAbjwyLHsvpM39jczs47BiapZBXv5jQ18/69zARgzqBefO2pMxhGZmZm1HieqZhUqIvh/f3iOzdvqAbjyY/tS06U646jMzMxaT0UmqpJqJF0pabGkTZKmSTqmiW2HS7pN0mpJayXdIeld50tKulDSFEmvSQpJN5W439np9WJfQ4rUP0nS05Jq03t/U1KXZv8QrNO77cmF/HPBKgDOOHgXJo4akHFEZmZmratSE6SbgMnA94F5wNnA3ZKOiojHSjWS1Bt4CNgBuALYClwMPCJp/4hYmVf9MqAPMB0Y2oSY/hN4uaBsdcHzjwNuBx4GvgDsA1wODAYubMIzzABYuX4zV9w9B4AhfbvzHx/cI+OIzMzMWl/FJaqSDgJOBS6NiKvSspuBWcD3gEMbaH4RsBtwUEQ8kba9J237JeAreXWPAF6LiJC0vgmh3RMRTzZS5yrgOeADEbEtff5a4CuSfhARc5rwHDP++545rNm0FYBvnDSOPt27ZhyRmZlZ66vEof/JQB1wfa4gImqBG4FDJI1opO0TuSQ1bTsHeAA4Jb9iRLwaEdGcwCT1kVR0kqCkvYG9getzSWrqOkBpbGaNevKVVUx5ahEAR+85mGPH7ZRxRGZmZm2jEhPVCcDciFhbUD49fd2/WCNJVcC+QLFez+nAGEnbs6/PQ8BaYKOkOyXtVnB9Qvr6judHxGJgUd71oiQNljQu/wvwEu9OZltdPZffPguAmi5VfOND45C8Z6qZmXVMFTf0TzJfdEmR8lzZsBLtBgA1TWj7YjPj2UgyZzaXqL4HuAR4XNIBEbEwL+78ZxU+v1TcORcBX29mbNbB3PT4K8xZug6Azx81lpE79sw4IjMzs7ZTiYlqD2BzkfLavOul2tHCtiVFxG3AbXlFt0u6F3gU+Crw2SY+v28jj7oOmFJQNga4o1kBW8VauqaWa+5P9kzddWAvzj/iXZtVmJmZdSiVmKhuIukZLdQ973qpdrSwbbNExGOSpgHvb8bzG3x2RCwHlueXeci3c/mvu55nw5Y6AL550jjvmWpmZh1eJc5RXULx7aJyZYtLtFtF0pvZkrYtsZBkukFObsi/1PNb89nWwTw27w3+/FzyR+iEfYdy+O6DMo7IzMys7VViojoD2F1S4VD5pLzr7xIR9cBMYGKRy5OABRGxrpViBBgNrMh7n4vrHc+XNAzYmRJxm23ZVs9/3pksoOrVrZqvnbB3xhGZmZm1j0pMVKcC1cD5uQJJNcA5wLTc4iVJIyXtWaTtgZIm5rXdAziad8//bBJJ7+raknQ8yaKqv+TKImI2MAc4v2ALqwuBSGMze5eb//EKC1ZsAOCL79+dITt0b6SFmZlZx1Bxc1QjYpqkKcB3JQ0G5gNnAaOAc/Oq3kyyaX/+RM7rgPOAuyRdRXIy1SXAMuDq/OdI+hCwX/q2K7CvpMvT93dGxHPp949LeoZk26k1wAHAp0mG/q8oCP9S4E7gPkm3AuOBzwM/i4gXmvmjsE5gxbrN/OCv8wAYPagXZx06KtuAzMzM2lHFJaqpM4FvA2cA/UlOezoxIh5tqFFErJN0JHANydGlVSTHmV4cESsKqn+MJAHOmcDbe50uSp8J8DvgBOADQE+Suag3AN+MiGUFz/+zpI+SbDP1Q5KpAVcA32rCZ7ZO6Or7XmTd5uR8iP88cW+6danEQRAzM7OWUTMPX7Iykm76P2vWrFmMGzcu63Cslc16fQ0f+tFjRCQnUP387AOzDsnMzKzFZs+ezfjx4wHGp1MiG+XuGbMyFBF8487ZRECXKnH5CXtlHZKZmVm7c6JqVob+9NwSnnz1TQDOee8oRg/qnXFEZmZm7c+JqlmZ2bSlju/enaytG9i7G1/4l90yjsjMzCwbTlTNysxPHnmJJWuSU30vPXYP+nbvmnFEZmZm2XCialZGlqzZxP898hIA44f3ZfJ7RmQckZmZWXacqJqVkavuncvmbfUA/OeJ46iuUiMtzMzMOi4nqmZlYvbiNfzhmUUAHDtuJw7adUDGEZmZmWXLiapZGYgIrrj7hbe2o7rsg4Wn/5qZmXU+TlTNysDDc1fw9/krAfjUpJHejsrMzAwnqmaZ21ZX/9Z2VH1quvCv3o7KzMwMcKJqlrmpTy1i7rL1AFx41Bh27F2TcURmZmblwYmqWYY2bN7G/94/F4BhO3Tn0+/dNeOIzMzMyocTVbMM3fC3BSxftxmAfz92D7p3rc44IjMzs/LhRNUsIyvWbeb6RxcAMG5YXz68//CMIzIzMysvTlTNMvKjB+excUsdAF85fi+qvLm/mZnZOzhRNcvAwlUb+c301wA4bLeBvHfswIwjMjMzKz9OVM0ycM39c9laFwBceuweGUdjZmZWnpyomrWzF5eu448zXgfg+H2GsO/O/bINyMzMrEw5UTVrZ/9z74tEQHWV+NIH3JtqZmZWihNVs3b01Ktv8tcXlgEw+YCdGeOjUs3MzEpyomrWTiKC7/1lDgDdulTxb+/3UalmZmYNcaJq1k4enfcG015eBcCZB+/CsH49Mo7IzMysvDlRNWsH9fVv96b2runCRUeNzTgiMzOz8udE1awd3Dt7KbMXrwXgvMNGM6BXt4wjMjMzK39OVM3aWH198P2/zgOgf8+unHvYrhlHZGZmVhmcqJq1sXtmLeXFZesAOO/w0fSu6ZJxRGZmZpXBiapZG6qrD77/17kADOjVjbMOGZVtQGZmZhWkIhNVSTWSrpS0WNImSdMkHdPEtsMl3SZptaS1ku6QNLpIvQslTZH0mqSQdFOJ+/2LpJ9Lmitpo6QFkn4maWiRug+n9yr8+kuzfwhWEe6auYR5y9cDcMHho+nl3lQzM7Mmq9S/NW8CJgPfB+YBZwN3SzoqIh4r1UhSb+AhYAfgCmArcDHwiKT9I2JlXvXLgD7AdOBdSWeeK4EBwJQ0ltHA54ET03suLai/CPh/BWWLG7i/Vai6+uAHaW/qwN7dOOOQXTKOyMzMrLJUXKIq6SDgVODSiLgqLbsZmAV8Dzi0geYXAbsBB0XEE2nbe9K2XwK+klf3COC1iAhJ6xu45yXAYxFRnxfjX4BHSBLWywvqr4mIXzf6Qa3i/fm5xby0YgMAFxw+hp7dKu4/NzMzs0xV4tD/ZKAOuD5XEBG1wI3AIZJGNNL2iVySmradAzwAnJJfMSJejYhoLJiIeDQ/Sc2VAauAvYq1kdQl7d21DmpbXT0/SFf6D+xdw+kHuzfVzMysuSoxUZ0AzI2ItQXl09PX/Ys1klQF7As8WeTydGCMpD6tEWCahPYG3ihyeXdgA7BO0lJJ35bUtTWea+XjzmcXs+CNpDf1s0eMpke36owjMjMzqzyVOBY5FFhSpDxXNqxEuwFATRPavrhd0SW+CHQDfldQ/hLJHNmZQC+SHt7LSZLXTzR0Q0mDgUEFxWNaIVZrZdvq6rn2gaQ3dVAf96aamZm1VCUmqj2AzUXKa/Oul2pHC9s2maTDga8Dt0XEg/nXIuLcguq/knQ9cJ6kayLinw3c+qL0vlbmbp+xmFdWbgTgoiPH0L2re1PNzMxaohKH/jeR9IwW6p53vVQ7Wti2SSTtCfyRZHHWZ5rY7Or09f2N1LsOGF/wdXILwrQ2tDWvN3WnvjV88qCRGUdkZmZWuSqxR3UJMLxIeW4LqVJbPa0i6U0tttVUY20blS7iug9YAxwfEeua2HRh+jqgoUoRsRxYXvDM5oZpbeyOGYt5bVWuN3Wse1PNzMy2w3YnqpJ6AoeRbOd0CMk8z0EkvZQrgRXACyTbNT0SEds7B3QGcJSkvgULqiblXX+XiKiXNBOYWOTyJGBBM5LLd5C0I0mSWgP8S0QUmwdbSu6wgRUtebaVj7r64LqH5gNJb+onDmxoAwozMzNrTIuH/iUdIOn/gKXA3SQb5B9Bsk9pP5JEdTjJKvxPAj8Bnpf0pKTzJPVq4aOnAtXA+Xmx1ADnANMiYmFaNjIdii9se6CkiXlt9wCOJtmwv9nSz3E3yWc9PiLmlajXN40zv0y8vc/qvS15vpWPe2YteWul/3mHjXZvqpmZ2XZqdo+qpP2Bq4CjgNzYcy3wNPAMyZZMq0jmew5Iv3Yl6bUcCRwA/BT4nqQrgB9ExJamPj8ipkmaAnw3XQk/HzgLGAXkL1a6mSRxzh8fvw44D7hL0lUkJ1NdAizj7bmiuc/5IWC/9G1XYF9JuaTyzoh4Lv3+FuAg4OfAXpLy905dHxG3p98fAPxW0m/TmHsAHwHeC1wfEU839Wdg5Sci+PFDLwEwoFc3TpvkualmZmbbq1mJqqRfAp8i6Yl9g2T7pd+QbKK/rQntdwJOAk4H3gf8N3ChpLMi4m/NCOVM4NvAGUB/4DngxHSj/ZIiYp2kI4FrSHoyq4CHgYsjonDo/WMkCXDOhPQLkmNQc4nq/unrp9OvfK8Ct+d9/zeS5HQIUE8yJeKz5B1eYJXpwTnLeWFJMhPl0+8d5VOozMzMWoGacPjS25WleuB5kiRxakTUtfjB0i7Al0mG7K+IiG+19F6dlaRxwKxZs2Yxbty4rMPptCKCj/7kcZ55bTV9arrw2JePZocePsPBzMws3+zZsxk/fjzA+IiY3ZQ2zZ2j+klgn4j43fYkqfDWEaUXAmNJjjA1q0j/eGklz7y2GoCzDh3lJNXMzKyVNGt8MiIKT1rabhGxiGQo3awi/fDBZKV/j67VfPp9u2YcjZmZWceRyYb/kjyBzzqEp159k38sWAnAaZNGMqBXt4wjMjMz6zjaNVGVtLekq3EPqnUQP073Te1WXcX5h49upLaZmZk1R5v3bErqTTK39VzgwLZ+nll7mb14DQ/OSQ4LmzxxZ3bq272RFmZmZtYcbZaoSjqMJDmdTLJnaG4/0+XAH9rquWbt5bp039TqKnHhEWMyjsbMzKzjadVEVdIQ4GySLafG5opJjgf9A3AbyTGq9a35XLP29tKK9dw9Kzkp9+T9hzFiQM+MIzIzM+t4tjtRlVQNfIik9/RYkuNNRXJaVXcggDERsX57n2VWLq5/ZAERIMFFR7o31czMrC20OFGVtCdJcno6MJi3h/YfB35J0nv6ZlrmHlTrMJatreWPz7wOwAf23omxg/tkHJGZmVnH1OxEVdK5JEeFHpwrIjke9Gbg5oh4Ka9ua8RoVlZ+8fdX2FKX/Nvr/MPdm2pmZtZWWtKjegPJcP564PfALyPikVaNyqxMravdyi3TXgXgwFH9ec8u/TOOyMzMrOPanjmqU4GfRsQTrRWMWbm7dfpC1tVuA9ybamZm1tZasuH/EyTD/WcD/5Q0W9JlknZu1cjMysyWbfXc+NjLAIwZ1It/2XNwxhGZmZl1bM1OVCNiErAPcC2wEtgLuAJ4RdJ9ks6Q5L16rMP507OLWbq2FoALDh9DVZXnYJuZmbWlFh2hGhGzI+JiYDjwCeD+9NL7gZuApZJ+2SoRmpWBiOD6RxcAMLhPDSdPGJZxRGZmZh1fixLVnIjYGhFTIuKDwCjgGyQ7APQm2bYq0qrfluTjU61iPTx3BS8uWwfAOe/dlZou1RlHZGZm1vFtV6KaLyIWRcS3ImI0Sc/q74DNJPNZv0gyn3WBpO9JmthazzVrD//3SLLrWq9u1Zw2aWTG0ZiZmXUOrZao5ouIByPiNGAY8K/AsyQJ6yjgS8A/2+K5Zm3h2YWr+eeCVQCcNmkkO/TomnFEZmZmnUObJKo5EbE6In4UEQcA7wF+Aqzl7VOszMpebm5qlyrx6fftmnE0ZmZmnUebJqr5IuKZiPgcMAQ4o72ea7Y9XnljA/fMWgLASfsPY+gOPTKOyMzMrPPYng3/WyQiNgO/ae/nmrXEz//+MvXpksDzDx+dbTBmZmadTLN6VNtqf1Tvu2rlaM3GrUx5chEAh+8+iD2H9M04IjMzs86luUP/L0u6RFL31ni4pImS/kyywMqsrNz6xGts2loHwLmem2pmZtbumpuodgP+h+QUqv+WtE9zHyipt6QzJf0VmAYcD6xp7n3M2tK2unp++fgrAIwd3JvDdxuYbUBmZmadUHPnqI4l2dT/AuBS4FJJzwN/A6aTbEO1AlhFsodqf2AAMBo4CJgEHAH0IFn5/zzwHxFx9/Z+ELPW9JfZS1m8Jjku9dPv3RXJG1WYmZm1t2YlqhGxEviCpGtJhutPA8YBe5Mkr43J/W0/HfgxcEtE1DcnBrP28PPHXgagf8+ufPSA4RlHY2Zm1jm1aNV/RMwDPivp34FTgQ8ChwGDSjTZBjwNPAz8NiKebclzzdrDM6+9ydOvrQaSDf67d/VxqWZmZlnYru2pImI98LP0C0m7kpxGNQjoDqwkmQowLyI2bF+oZu3jxrQ3tUuVOPOQUdkGY2Zm1om16ob/EfFyRPw9Im6PiFsj4v6ImNHaSaqkGklXSlosaZOkaZKOaWLb4ZJuk7Ra0lpJd0h61waZki6UNEXSa5JC0k0N3LOfpOslrZC0QdJDkg4oUfckSU9Lqk3v/U1J7b6frRW3ePUm7pm1FIAT9x3KTn1bZYMLMzMza4F2O5mqld0EXALcAvwbUAfcLel9DTWS1Bt4iGRB1xXA14EJwCOSdiyofhlwNDCbZOpCqXtWAXeRzNf9EfAfwGDgYUm7FdQ9DrgdWA18If3+cuCHDX5aaze//Mcr1KU7/J/7Pm/wb2ZmlqWK68mTdBDJvNhLI+KqtOxmYBbwPeDQBppfBOwGHBQRT6Rt70nbfgn4Sl7dI4DXIiIkrW/gnpPTZ348Iqam97wNmAt8kySBzbkKeA74QERsS+uuBb4i6QcRMacJPwJrIxu3bOO3014D4MBR/dln5x0yjsjMzKxzq8Qe1ckkPajX5woioha4EThE0ohG2j6RS1LTtnOAB4BT8itGxKsREU2MZxnwh7y2K4DbgJMl1QBI2ptkd4Trc0lq6jqS3RAmN+FZ1oZ+/9Qi1tYmvxpv8G9mZpa9SkxUJwBzI2JtQfn09HX/Yo3SIfp9gSeLXJ4OjJHUp4XxPF1km63pQE9g97x6FD4/IhYDi/KuFyVpsKRx+V/AmBbEa0XU1we/+PsrAOzcvwfH7D0k24DMzMys8ob+gaHAkiLlubJhJdoNAGqa0PbFFsTzaCP3nJnWyy8vrFsq7pyLSObUWht4eO5yFryRrPk7+9BRVFd5g38zM7OsVWKi2oPk1KtCtXnXS7WjhW1bI57Gnt+3kedcB0wpKBsD3NGEGK0Rud7UXt2q+cSBDc0eMTMzs/ZSiYnqJpKe0ULd866XakcL27ZGPI09v8FnR8RyYHl+mY/1bB0vrVjP3+a9AcDHJ46gT/euGUdkZmZmUJlzVJfw9jB6vlzZ4hLtVpH0ZrakbWvEs6SgvLBuS55treBX/3j1re9PP3iXDCMxMzOzfJWYqM4AdpdUOFQ+Ke/6u6SLnWYCE4tcngQsiIh1LYzngHSxVuE9N5JsU5Uf1zueL2kYsHOpuK1tbdi8jd8/tQiAw3YbyNjBvTOOyMzMzHLaPFGV1EXSBEn7qXXGqqcC1cD5ec+oAc4BpkXEwrRspKQ9i7Q9UNLEvLZ7kGzsXzj/sznx7AR8NO+eA4GPA3+KiM0AETEbmAOcLyn/8PgLgUjvY+3sj8+8zrrNyZZUZ7g31czMrKxs9xzVNNH7BPBKRNxccO1I4DckiRzAQkmnRcTjLX1eREyTNAX4rqTBwHzgLGAUcG5e1ZtJNu3PT46vA84D7pJ0FbCV5ISrZcDVBbF/CNgvfdsV2FfS5en7OyPiufT7qcA/gV+ke6W+QbJCv5p3r9K/FLgTuE/SrcB44PPAzyLihWb+KGw7RQQ3/+MVAIb368G/7LVTww3MzMysXbXGYqozgS9TkJRJ6g/8HuifVzySJEncKyKWbuczvw2ckd7/OeDEiCi2TdRbImJdmjxfQ3J0aRXwMHBxukl/vo+RJMA5E3h7r9NF6TOJiDpJxwP/A/wryer+J4CzI+IdW11FxJ8lfZTkZ/VDYAXJUa7fauLntlb0zwWrmLssOXTs9IN38ZZUZmZmZaY1EtWj09ffF5SfS5JEvgp8hmRV+09IehH/lXceV9os6UlUl6ZfpeocWaJ8EcmwfGPPOBs4u4nxvEnyGT/ThLq3A7c35b7WtnK9qd26VHlLKjMzszLUGnNUh6evLxWUn0wy9/L/RcQD6XD/hSRD8ce2wnPNWmzx6k3c9/wyAE7abxgDenXLOCIzMzMr1BqJ6iBgdURsyRVI6gocCGwD/pQrT5PVbcDYVniuWYv9Ztpr1NUHAGce4kVUZmZm5ag1EtV6oFdB2QSgG/BsRGwouLaG4pvem7WLzdvquPWJ1wDYf0Q/9t25X7YBmZmZWVGtkaguArpK2iuv7IT09e/5FdPtqfqSrIw3y8Q9M5fyxvpkAOCsQ92bamZmVq5aI1F9hGTe6dWSBkvaH/gsyfzUuwvq7kGy1ZNPYbLM/DJdRLVjr24cv0+xg8LMzMysHLRGono1ydGkx5IcE/oUybzVZyPi/oK6H0xfp7fCc82a7blFq3nmtdUAfPKgkdR0qW64gZmZmWVmuxPVdK/Qk4CXSXpWA7ifZNV/oXPS14e297lmLXHzP14FoEpw2qSRGUdjZmZmDWmNfVRJe07HShoErEv3OX2HdCeAf03fPtEazzVrjjUbt/KnZ5NZJ8fsvRPD+vXIOCIzMzNrSKskqjlFTnfKv7aVZD6rWSb+8MwiNm+rB5KTqMzMzKy8tcYcVbOyFxHcMi3ZkmrkgJ68d8zAjCMyMzOzxjSrR1VSq03qi4jXWuteZo154pU3mb98PZDMTa2qUsYRmZmZWWOaO/T/cis9N1rwbLMWu2Vasoiqa7WY/J6dM47GzMzMmqK5yWJrdUO5O8vazaoNW7hn5lIAPjh+KAN7+2A0MzOzStDcRHXXEuUHAf9H0lP6U+BBkhOrAIYDR5McAiDgArzq39rR1KcWsqUuWUT1KW9JZWZmVjGalahGxKuFZZLGANcDC4FjImJZQZUXgQclXQv8FbgBOKBl4Zo1T3198Jt0EdWYQb2YtOuAjCMyMzOzpmqNVf9fBfoC5xVJUt8SEcuB84AdgMtb4blmjfrHgpW8snIjAKdN2gXJs07MzMwqRWskqscA6yNiWmMV0zrr0zZmbS63iKpblyo+dsDwjKMxMzOz5miNRHUQ0JwD06vSNmZtavm6Wu6bnXTyn7jvUPr17JZxRGZmZtYcrZGoLgd6SDq6sYppnZ5AyROszFrLlCcXsa0+APjUJJ9EZWZmVmlaI1G9h2Q1/42Sdi9VSdJuwM9Idga4pxWea1ZSXX3w2+nJIqo9h/ThgJH9sg3IzMzMmq01Nt3/JjAZGAk8K2kKyfZUr6fXhwNHAR8HugNvAt9qheealfTovBUsenMTkGxJ5UVUZmZmlWe7E9WIWCzpGOCPwAjgU+lXIZHsrfqRiHi9yHWzVnPLP5Pe1B5dqzl5ghdRmZmZVaLWGPonIp4GxgFfBmYAdSSJqYD6tOzLwLiIeKo1nmlWyvK1tTz04nIATtpvGH27d804IjMzM2uJ1hj6ByAi1gPfA74nqSuQ21l9VURsba3nmDVm6tOLqEsXUZ160IiMozEzM7OWarVENV+amJbc/N+srUQEtz2xEIDdd+rN/iP6ZRuQmZmZtVirDP2blYtpL6966ySqTxzoRVRmZmaVzImqdSi/S3tTu1aLj3gRlZmZWUVrdqIqqa4VvrZtT9CSaiRdKWmxpE2SpqU7DzSl7XBJt0laLWmtpDskjS5R91xJL0iqlTRP0heK1HlFUpT4mldQt1S9L7fsJ2H51mzayt0zlwDwgXFDGNDLJ1GZmZlVspbMUS2HsdSbSPZu/T4wDzgbuFvSURHxWKlGknoDDwE7AFcAW4GLgUck7R8RK/PqXgD8FPg98L/AYcC1knpGxJV5t/0i0LvgUbsA/wXcVySM+4GbC8qeKf1RranunPE6m7fVA3DqgV5EZWZmVulakqh+s9WjaAZJBwGnApdGxFVp2c3ALJJdBw5toPlFwG7AQRHxRNr2nrTtl4CvpGU9gO8Ad0XE5LTtDZKqgK9Juj4i3gSIiNuLxHh5+u0tRWKYGxG/bvontqa6NR32H96vB+8dMzDjaMzMzGx7NTtRjYhME1WSntQ64PpcQUTUSroRuELSiIhY2EDbJ3JJatp2jqQHgFNIE1WSk7R2BK4raP9jksMMTgAaSjZPA16OiMeLXUwT4YiI2gbuYc0w6/U1zF68FoBTJo6gqqocOv7NzMxse1TiYqoJJL2SawvKp6ev+xdrlPaG7gs8WeTydGCMpD55z6BI3adIDjCYQAmSJgB7Ab8pUeVsYAOwSdLzkk4rdS9rutwiKgk+PnHnjKMxMzOz1tAm+6i2saHAkiLlubJhJdoNAGqa0PbF9Bl1EbE8v1JEbJG0soFnwNvHxxYb9n8cuA14Ob3H54BbJO0QET9p4J5IGgwMKige01CbzqJ2ax23z0hO5T18t0EM69cj44jMzMysNVRiotoD2FykvDbveql2NLFtD2BLifvUlnpG2mt7KvBMRLxQeD0i3ltQ/+ckvbRXSLopIjaVeCYk82u/3sD1TuueWUtYV5tsJOFFVGZmZh1HJQ79byLpGS3UPe96qXY0se0moNTeRt0beMYRwHCK96a+S0RsAX4E9APe00j164DxBV8nN+U5Hd2t05Nh/x17deNf9top42jMzMystVRij+oSkmSw0ND0dXGJdqtIelOHFrlW2HYJUC1pcP7wv6RuJIusSj3jUyRzWH9bMvp3yy38GtBQpTSOd0xF8KlL8PIbG5j28ioAPnrAcLp1qcR/e5mZmVkxlfi3+gxgd0l9C8on5V1/l4ioB2YCE4tcngQsiIh1BfcorDuR5Gf2rmdIqgE+BjwcEaUS2WJyhw2saEYbS9325NsbPHzCw/5mZmYdSiUmqlOBauD8XEGaJJ4DTMttTSVppKQ9i7Q9UNLEvLZ7AEcDU/LqPUjSA3thQfsLgY3AXUXiOp5kCL/osL+kwoVQpLsMfBF4g2SuqjXDtrp6fv/UIgAm7tKfsYP7NNLCzMzMKknFDf1HxDRJU4Dvpivh5wNnAaOAc/Oq3kwyZzR/fPw64DzgLklXkZxMdQmwDLg67xmbJH0N+HH6rHtJTqY6HfhqRKwqEtqnSKYW/L5E6J+T9GHgT8BrJNMNPg2MBM5I56taM/xt3hssX5esjfOWVGZmZh1PxSWqqTOBbwNnAP2B54ATI+LRhhpFxDpJRwLXAJeT9Cg/DFwcESsK6l4naSvJiVUnkcwlvRj4QeF902kIJ5CcZLWmxOP/TnJq1mdI5rluINm/9dMR8WCjn9jeZWram9qjazUn7NvQjmFmZmZWiSoyUU1PdLo0/SpV58gS5YuAjzfxOTcANzSh3lpKb4uVq3M/cH9TnmuNW71xC/c/vwyA48YPoXdNRf5RNjMzswZU4hxVM/707GK21NUDMPk9HvY3MzPriJyoWkXKDfsP79eDg0fvmHE0ZmZm1hacqFrFmbdsHc8uSqYCf+yA4VRVeT9ZMzOzjsiJqlWcqU8veuv7j3nY38zMrMNyomoVZVtdPX98+nUADho1gF127JVxRGZmZtZWnKhaRfnb/Lf3TvUiKjMzs47NiapVlPy9U4/fd2jG0ZiZmVlbcqJqFWPNxq3cP9t7p5qZmXUWTlStYtz5nPdONTMz60ycqFrF8N6pZmZmnYsTVasI85at49mFqwH4qPdONTMz6xScqFpFeMfeqQd42N/MzKwzcKJqZS9/79QDR/Vn1EDvnWpmZtYZOFG1svf4Syvf2jvVvalmZmadhxNVK3t/fCbpTe3WpYrj9vHeqWZmZp2FE1Uraxs2b+Mvs5YCcMxeO7FDj64ZR2RmZmbtxYmqlbX7nl/Kpq11AHx4wvCMozEzM7P25ETVytof0kVU/Xt25YjdB2UcjZmZmbUnJ6pWtpavreXv898A4MR9h9Gti/+4mpmZdSb+m9/K1p3PLqY+ku8/coCH/c3MzDobJ6pWtnKr/Uft2JMJI/plG4yZmZm1OyeqVpbmLlvH7MVrgWQRleQjU83MzDobJ6pWlnK9qQAf8Wp/MzOzTsmJqpWd+vrgjjRRPWBkP3bZ0UemmpmZdUZOVK3sTHt5FYvX1ALwER+ZamZm1mk5UbWy88dnFgHQtVqc6CNTzczMOi0nqlZWarfWcc/M5MjUI/cYTP9e3TKOyMzMzLLiRNXKyl9fWMa6zdsAL6IyMzPr7CoyUZVUI+lKSYslbZI0TdIxTWw7XNJtklZLWivpDkmjS9Q9V9ILkmolzZP0hSJ1viEpinzVtvSendkf0yNT+3TvwtF7Ds44GjMzM8tSl6wDaKGbgMnA94F5wNnA3ZKOiojHSjWS1Bt4CNgBuALYClwMPCJp/4hYmVf3AuCnwO+B/wUOA66V1DMirixy+wuB9Xnv64o8v7n37FRWbdjCI3NXAHDCPkPp3rU644jMzMwsSxWXqEo6CDgVuDQirkrLbgZmAd8DDm2g+UXAbsBBEfFE2vaetO2XgK+kZT2A7wB3RcTktO0NkqqAr0m6PiLeLLj31Ih4o4G4W3LPTuXumUvYlp6Z+mEP+5uZmXV6lTj0P5mkt/L6XEFE1AI3AodIGtFI2ydySWradg7wAHBKXr2jgB2B6wra/xjoBZxQ5N6S1Felj1BqyT07lTtnLAZgSN/uHDRqQMbRmJmZWdYqMVGdAMyNiLUF5dPT1/2LNUp7LvcFnixyeTowRlKfvGdQpO5TQH3e9XwLgDXAOkm/lrRTkbibe8/8+AdLGpf/BYxpqE0lWbx6E9NfWQXAh/YbSlWVj0w1MzPr7Cpu6B8YCiwpUp4rG1ai3QCgpgltX0yfURcRy/MrRcQWSSsLnvEm8CPgH8BmknmnnwMOkjQxL6Fuzj2LuQj4eiN1Ktafn1v81vcn7edhfzMzM6vMRLUHSUJYqDbveql2NLFtD2BLifvU5j8jIn5QcP33kqYDt5Akl//d3HuWcB0wpaBsDHBHI+0qwh3psP/ogb0YP7xvxtGYmZlZOajEof9NJD2jhbrnXS/Vjia23QSU2mm+ewPPACAifgMsBd5f8PztuefyiJid/wW81FCbSvHSivXMXpx0PH9ov2GUnuZrZmZmnUklJqpLSIbRC+XKFhe5BrCKpDe1KW2XANWS3rGRp6RuJAuiSj0j30KS6QY5rXHPDim3iArgpP0bmwFhZmZmnUUlJqozgN0lFY4PT8q7/i4RUQ/MBCYWuTwJWBAR6wruUVh3IsnPrOgzctKV/6OAFQVxt/ieHVVEcOezSaI6fnhfxgzqnXFEZmZmVi4qMVGdClQD5+cKJNUA5wDTImJhWjZS0p5F2h4oaWJe2z2Ao3nn/M8HSXpgLyxofyGwEbgrr/2gIjFeCAwC/tKSe3Yms15fy8tvbADgpP3cm2pmZmZvq7jFVBExTdIU4LvpMPp84CySHsxz86reDBwB5E94vA44D7hL0lUkJ1NdAiwDrs57xiZJXwN+nD7rXpLV/KcDX42IVXn3fFXS70h6a2uB95EcSDAD+L8W3rPTuPPZ5MhUKZmfamZmZpZTcYlq6kzg28AZQH/gOeDEiHi0oUYRsU7SkcA1wOUkPcoPAxdHxIqCutdJ2kpyYtVJJHNOLwYKV/nfQnIa1sdIFkW9SnJC1nciYmML79kp1NcHf3o22RnswFEDGLpDYxsfmJmZWWdSkYlqehLVpelXqTpHlihfBHy8ic+5AbihkTrnNeVezblnZzH9lVUsXZvsDHayF1GZmZlZgUqco2odRG4RVZcqcfz4YpsxmJmZWWfmRNUysWVbPXfPTIb9D999EP17ldpi1szMzDorJ6qWicfmr2D1xq2AV/ubmZlZcU5ULRO5Tf67d63imL13yjgaMzMzK0dOVK3dbdpSx33PLwPg/XvtRK+ailzTZ2ZmZm3Miaq1uwfnLGfjljrAw/5mZmZWmhNVa3d3zUyG/fvUdOGIPYod7GVmZmbmRNXa2YbN23hwznIAjhm3EzVdqjOOyMzMzMqVE1VrVw/MWU7t1noATtzXe6eamZlZaU5UrV3d9Vw67N+9C+8b62F/MzMzK82JqrWb9Zu38dCLKwA4dtwQunXxHz8zMzMrzZmCtZsHXljGlm3JsP8JHvY3MzOzRjhRtXbz5+eSI1N36NGV944ZmHE0ZmZmVu6cqFq7WFu7lUfeGvbfycP+ZmZm1ihnC9Yu/vr8MrbU5Vb7e5N/MzMza5wTVWsXd6XD/v17duWQMTtmHI2ZmZlVAieq1ubWbNrKo/OSYf8Pjh9C12r/sTMzM7PGOWOwNnf/88vYWhcAnLCPh/3NzMysaZyoWpvLbfI/oFc3Dh49IONozMzMrFI4UbU2tXrjFv427w0gGfbv4mF/MzMzayJnDdam7pu9jG31ybD/id7k38zMzJrBiaq1qT/PTFb7D+zdjUm7erW/mZmZNZ0TVWszb27Ywt/nJ8P+x40fSnWVMo7IzMzMKokTVWsz985eSl067H+Ch/3NzMysmZyoWpv5c7rJ/6A+NRw4yqv9zczMrHmcqFqbWLl+M4+/lAz7Hz9+iIf9zczMrNmcqFqbuHf2MtJRf07Y15v8m5mZWfM5UbU2cc+st4f9J+7SP+NozMzMrBJVZKIqqUbSlZIWS9okaZqkY5rYdrik2yStlrRW0h2SRpeoe66kFyTVSpon6QtF6nxU0u8kLZC0UdKLkq6W1K9I3VckRZGvnzb7h1DG1mzcyj9eWgnAseN2osrD/mZmZtYCXbIOoIVuAiYD3wfmAWcDd0s6KiIeK9VIUm/gIWAH4ApgK3Ax8Iik/SNiZV7dC4CfAr8H/hc4DLhWUs+IuDLvttcDi4FfA68B+wCfB46XdEBEbCoIYwZwdUHZ3KZ+8Epw/wtvb/J/3Hiv9jczM7OWqbhEVdJBwKnApRFxVVp2MzAL+B5waAPNLwJ2Aw6KiCfStvekbb8EfCUt6wF8B7grIianbW+QVAV8TdL1EfFmWj45Ih4uiPEp4JfAp4CfFcTwekT8utkfvIL8JR3279+zK5N29Wp/MzMza5lKHPqfDNSR9GQCEBG1wI3AIZJGNNL2iVySmradAzwAnJJX7yhgR+C6gvY/BnoBJ+S1f7jIc/6Yvu5VLAhJ3ST1aiDOirV+8zYenZes9j9m753oUl2Jf8TMzMysHFRiFjEBmBsRawvKp6ev+xdrlPaG7gs8WeTydGCMpD55z6BI3aeA+rzrpQxJX98ocu1oYCOwPp2z+m+N3AsASYMljcv/AsY0pW17enDOcrZsqwc87G9mZmbbp+KG/oGhwJIi5bmyUnshDQBqmtD2xfQZdRGxPL9SRGyRtLKBZ+RcRtLrO7Wg/DngsfQZO5LMrf2+pGERcVkj97wI+HojdTKXG/bvU9OFQ8fumHE0ZmZmVskqMVHtAWwuUl6bd71UO5rYtgewpcR9aht4BpJOA84FvhcR8/KvRcRJBXV/AdwDXCLphxGxqNR9SaYhTCkoGwPc0UCbdrVpSx0PzVkBwL/sNZiaLtUZR2RmZmaVrBIT1U0kPaOFuuddL9WOJrbdBHQrcZ/upZ4h6TCSubL3Al8t0f4tERGSrgGOBY4k2TmgVN3lwDt6eKXy2vbpkbkr2LS1DoAPetjfzMzMtlMlzlFdQjI0XyhXtrhEu1UkvalNabsEqJY0OL+SpG4kQ/bveoak/YA7SXYQmBwR2xr4DPkWpq8Vvzw+N+zfo2s1R+w+KONozMzMrNJVYqI6A9hdUt+C8kl5198lIuqBmcDEIpcnAQsiYl3BPQrrTiT5mb3jGZLGAH8h6fE8PiLWN/IZ8uUOG1jRjDZlZ/O2Oh54IenwPWrPQfTo5mF/MzMz2z6VmKhOBaqB83MFkmqAc4BpEbEwLRspac8ibQ+UNDGv7R4kK/Hz538+SNIDe2FB+wtJVuzfldd+CHAfyW4Ax0ZE0YRT0gBJ1QVlXYEvk8yHfajhj13eHp+/knWbk05kD/ubmZlZa6i4OaoRMU3SFOC76dD8fOAsYBTJIqacm4EjgPyJnNcB5wF3SbqK5GSqS4Bl5J0WFRGbJH0N+HH6rHtJTqY6HfhqRKzKu+dfSHpFvwe8T9L78q4ti4j70+9PAi6XNBV4mWSo/zRgPPCViFjawh9JWbgnHfbv1qWKo/cc3EhtMzMzs8ZVXKKaOhP4NnAG0J9k26cTI+LRhhpFxDpJRwLXAJeT9Cg/DFxc2BMaEddJ2kpyYtVJJHNJLwZ+UHDb/dLX/yjyyEeAXKI6E3ieJNkdRNKLOgM4JSIKV/NXlG119dz//DIADt9tIL1rKvWPlZmZmZWTiswo0pOoLk2/StU5skT5IuDjTXzODcANjdRp0tL7iHiKJOHtcKa9vIo3N24FPOxvZmZmracS56hamckN+3epEsfstVPG0ZiZmVlH4UTVtkt9fXDv7GTY/5AxO7JDz64ZR2RmZmYdhRNV2y5PvfYmK9Ylh30d52F/MzMza0VOVG273DMz2aygSvCBcR72NzMzs9bjRNVaLCK4d3aSqB44agADexc7ndbMzMysZZyoWos9t2gNr6/eBMBx44dkHI2ZmZl1NE5UrcVyvakAxzpRNTMzs1bmRNVa7L50k//9RvRj6A49Mo7GzMzMOhonqtYiL61Yz/zl6wH4wN5eRGVmZmatz4mqtUjuyFSAY73a38zMzNqAE1VrkfvS+amjB/ZizKDeGUdjZmZmHZETVWu25WtreWbhagCOGbcTkrINyMzMzDokJ6rWbH99YTkRyfcf2Nur/c3MzKxtOFG1Zrvv+WTYf2DvGiaM6JdtMGZmZtZhOVG1ZllXu5XH568E4Ji9d6KqysP+ZmZm1jacqFqzPDJ3BVvq6gH4gFf7m5mZWRtyomrNct/sZFuqXt2qOXTMjhlHY2ZmZh2ZE1Vrsi3b6nloznIAjtxzMDVdqjOOyMzMzDoyJ6rWZP9csJJ1m7cBPo3KzMzM2p4TVWuy3Gr/rtXiqD0HZxyNmZmZdXROVK1J6uvjrWNTDx69I327d804IjMzM+vonKhakzz3+hqWrd0MwAfGeZN/MzMza3tOVK1J7pu99K3vj9nL81PNzMys7TlRtSbJDfvvN6IfQ3bonnE0ZmZm1hk4UbVGLVixnnnL1wNe7W9mZmbtx4mqNSrXmwpwrE+jMjMzs3biRNUadV+aqI4e2Isxg3pnHI2ZmZl1FhWZqEqqkXSlpMWSNkmaJumYJrYdLuk2SaslrZV0h6TRJeqeK+kFSbWS5kn6Qnvds1wsX1fL06+9CcAx43ZCUsYRmZmZWWdRkYkqcBNwCXAL8G9AHXC3pPc11EhSb+Ah4AjgCuDrwATgEUk7FtS9APgZMBv4AvAP4FpJl7X1PcvJAy8sJyL5/lhvS2VmZmbtqEvWATSXpIOAU4FLI+KqtOxmYBbwPeDQBppfBOwGHBQRT6Rt70nbfgn4SlrWA/gOcFdETE7b3iCpCviapOsj4s02vGfZyG1LNahPDfvv3C/bYMzMzKxTqcQe1ckkPajX5woioha4EThE0ohG2j6RSyjTtnOAB4BT8uodBewIXFfQ/sdAL+CENr5n2fjmSeO5/IS9uODw0VRVedjfzMzM2k8lJqoTgLkRsbagfHr6un+xRmnP5b7Ak0UuTwfGSOqT9wyK1H0KqM9db4t7lpuRO/bkM4eN5jOHFZ1ya2ZmZtZmKm7oHxgKLClSnisbVqLdAKCmCW1fTJ9RFxHL8ytFxBZJK/Oe0Rb3LErSYGBQQfGYhtqYmZmZVbJKTFR7AJuLlNfmXS/Vjia27QFsKXGf2oJ6rX3PUi4iWahlZmZm1ilUYqK6iaQXs1D3vOul2tHEtpuAbiXu072gXmvfs5TrgCkFZWOAOxppZ2ZmZlaRKjFRXQIML1I+NH1dXKLdKpKez6FFrhW2XQJUSxqcP1QvqRvJgqhcvba4Z1Fpm3dMG/CepmZmZtaRVeJiqhnA7pL6FpRPyrv+LhFRD8wEJha5PAlYEBHrCu5RWHciyc9sRlvd08zMzMwSlZioTgWqgfNzBZJqgHOAaRGxMC0bKWnPIm0PlDQxr+0ewNG8c1j9QZLe0gsL2l8IbATuauN7mpmZmXV6FTf0HxHTJE0BvpuuhJ8PnAWMAs7Nq3ozyWlR+ePj1wHnAXdJugrYSnLC1TLg6rxnbJL0NeDH6bPuBQ4DTge+GhGr2vieZmZmZp1exSWqqTOBbwNnAP2B54ATI+LRhhpFxDpJRwLXAJeT9Cg/DFwcESsK6l4naSvJ6VInAQuBi4EftPU9zczMzAwUuYPcreJIGgfMmjVrFuPGjcs6HDMzM7OSZs+ezfjx4wHGR8TsprSpxDmqZmZmZtYJOFE1MzMzs7LkRNXMzMzMypITVTMzMzMrS5W66t8S3QDmz5+fdRxmZmZmDcrLV0odKf8uXvVfwSSdBNyRdRxmZmZmzXByRNzZlIpOVCuYpB1IDjVYCGxpo8eMIUmGTwZeaqNnWPP4d1Ke/HspT/69lB//TspTe/xeugEjgEciYk1TGnjov4Klv+Qm/YukpaS3DvZ6qal7nlnb8u+kPPn3Up78eyk//p2Up3b8vTzTnMpeTGVmZmZmZcmJqpmZmZmVJSeqZmZmZlaWnKhaY1YA30xfrTz4d1Ke/HspT/69lB//TspTWf5evOrfzMzMzMqSe1TNzMzMrCw5UTUzMzOzsuRE1czMzMzKkhNVMzMzMytLTlTNzMzMrCw5UbWiJNVIulLSYkmbJE2TdEzWcXVmknpL+qakv0haJSkknZ11XJ2ZpAMl/UjSbEkbJL0m6TZJu2cdW2cmaZykKZIWSNoo6Q1Jj0r6UNax2dskfTX9/9isrGPprCQdmf4Oin0dnHV8AF2yDsDK1k3AZOD7wDzgbOBuSUdFxGPZhdWpDQT+E3gNeBY4MtNoDOAy4L3AFOA5YAjweeBpSQdHhP8CzsYuQB/gl8BioCfwMeBOSRdExPVZBmcgaWfgK8CGrGMxAK4Fnigom59FIIW8j6q9i6SDgGnApRFxVVrWHZgFLI+IQ7OMr7OSVAP0j4ilkiaS/E/lnIi4KdvIOi9JhwJPRsSWvLLdgJnA1Ig4PbPg7B0kVQNPAd0jYs+s4+nsJN0KDAKqgYERMT7jkDolSUcCDwEfj4ip2UZTnIf+rZjJQB3wVq9DRNQCNwKHSBqRVWCdWURsjoilWcdhb4uIx/OT1LRsHjAb2CubqKyYiKgDFgL9Mg6l05N0OMnfM1/MOBTLI6mPpLIbaXeiasVMAOZGxNqC8unp6/7tG45Z5ZAkYCfgjaxj6ewk9ZI0UNIYSRcDxwEPZB1XZ5b2bP8Q+FlEzMw6HnvLL4C1QK2kh9JRu7JQdpmzlYWhwJIi5bmyYe0Yi1ml+RQwnGQ+sWXrauCC9Pt64A8kc4gtO58lmUP8/qwDMQC2AL8H7ib5x/XewL8Df5N0aEQ8k2Vw4ETViusBbC5SXpt33cwKSNoT+DHwD5KFPJat7wNTSf5xfQrJfMhuWQbUmUnaEfgW8O2IWJF1PJZMXwIezyu6U9JUksWh3wU+mElgeTz0b8VsAmqKlHfPu25meSQNAe4C1gCT0zmRlqGImBMRf42ImyPiRKA38Kd0eoa1v/8CVpEM/VuZioj5wB3AUelUjUw5UbVilpAM/xfKlS1ux1jMyp6kHYB7SBbqfDAi/N9IeZoKHAh4n9t2lu6GcT7JNkjDJI2SNIqkA6Rr+n5AljHaOywkGX3olXUgTlStmBnA7pL6FpRPyrtuZry1ddufSJKfEyPi+YxDstJy05Z2yDSKzmk4Sc5xLfBy3tckkv92XsbzusvJaJLpfuuzDsRzVK2YqSSTqc8Hcvuo1gDnANMiYmGGsZmVjXRY7HfAIcDJEfGPjEMyQNLgiFheUNYVOJNk6pL/MdH+ZgEfKVL+XySHM/wb8FK7RmRIGlQ4X1jSfsBJwD0RUZ9NZG9zomrvEhHTJE0BvitpMMnpFGcBo4Bzs4yts5P0eZLh5dzOCx9KT3gB+GFErMkksM7rapL/of8JGCDpHRv8R8SvM4nK/i8dEXoUeJ3kxLBPAXsCX4qIzHuJOpuIeAO4vbBc0hfT6++6Zu3id5I2kSyoWk6y6v98YCPw5SwDy/HJVFZUOpz5beB0oD/JCsCvRcS9mQbWyUl6hWRrl2J2jYhX2i8ak/QwcESp6xHhRTsZkHQqyT+q9wF2BNaRnEr1w4i4M8vY7J3S/4Z8MlVGJP0ryT/ixgJ9gRUkew1/M11UlTknqmZmZmZWlryYyszMzMzKkhNVMzMzMytLTlTNzMzMrCw5UTUzMzOzsuRE1czMzMzKkhNVMzMzMytLTlTNzMzMrCw5UTUzMzOzsuRE1czMzMzKkhNVMzMzMytLTlTNzDoYSd+QFOk56u397M+kz/5JG93/L+n9j26L+5tZeXGiambWxiTdkCZXKyXVNKPdvLTdnW0ZX2uR1Bv4NrAZ+E5e+f3p55jVjHv1lLQ2bXdt3qVvpK9XSfLfYWYdnP8jNzNrezemrwOAk5vSQNIRwNiC9uXuUmAI8LOIWJRXnot/nKSDmnivjwN9CtoTEf8E7gUmAKdvX7hmVu6cqJqZtbE0uXo+fXtOE5vl6i0D7mr1oFqZpB7Av6ZvC4f9/wisSr9v7ud/OiKeLbj20/T1P5oVpJlVHCeqZmbtI9cr+AFJwxuqKKkPMDl9e3NEbGvTyFrHJ4B+wIyImJ1/ISI2A7ekb0+V1L2hG0kaAxyevi3Wm3w3SeI7TtJ7tydoMytvTlTNzNrHr4CtJP/fPbuRup8AeqXf/7xYBUnHpXM/V0taL+lZSf8hqWtjgUj6hKR7JC2TtDW9xzxJd0r6XGOJZAnnpa+/KXE9l3D2Az7WyL3OAQTUFrtfRGwBfp++Pb9ZUZpZRXGiambWDiJiBZBbFHV2I9Vzw96PR8ScwouSvkHSq/h+YAeSBHhv4Ergr0C3UjeW9HPgVuCDwGCSZLAryXzYDwE/Ipln2mSSdgAOTt/+rViddPj+qfRtyeH/dIHUmenbP0TE6hJVH01fj21OrGZWWZyompm1n1yv4lhJhxerIGkP4NCC+vnXTwK+nr6dAoyMiP5AX+BzJAnjhSXu/T6SJLEeuAzYMSL6REQvYCBJ0vdLYEszP9f7SP4+2Qo800C93Oc5WtIuJeocA4woqF/MtPR1J0l7NjVQM6ssTlTNzNrPvUBuNfynS9TJla8Hbity/bvp6yPAqRGxECAiNkXEdSQLmvqVuHcuAf5rRHwvInILnIiIlRFxX0ScHRGLm/Rp3jYpfX0xnY9aym+ATSTD+qV6VXOf/2XgoVI3ioh5JD8jgEOaHqqZVRInqmZm7SQi6oGb0reT031H3yKpGjgjfXtbRKwvuL4vyRA/wH+l9yt0A/B6iRBWp6+D0me1lmHp64qGKkXEGt6eW3qWJOVfl9Sft7fv+kVERCPPXVnwfDPrYJyompm1r18AQbJY6hMF144DhqbfFxv2npi+bqP0XNB64OESz36AZE7qBOBvks6VtGuTIy9tUPq6qsFaidzisFFA4elSnwJqSKYm3NSEe+WeN6jBWmZWsZyompm1o4hYwNuJZOHwf+79nIh4vEjzwenrG40MsS8qVhgRLwGfIRkyPwT4GbBA0nJJv5N0cmEvZxPldgloKKach4EF6felPv99uSkNjdhU8Hwz62CcqJqZtb9cb+mhknYHkDQIODEtL7olVWuIiFuAXYDPAr8DFpL0SJ4C3A48IqlvM2+bG4Lv34TnB29/vo+kOwYgaT+Snl5o+ucfUPB8M+tgnKiambW/3/P2fNFcL+LpJNtEbQNuLtFuefo6UFLJLaiABg8UiIhVEfF/EXFqRIwk2Zrqv0mmJBwGfKOxD1AgNzd1QIO13nYTUAf0AD6ZluV+DiuBO5p4n9zzGpwba2aVy4mqmVk7i4j8jezPTBc25VbB/zkilpVo+mT62oUkoXyXdB/SI5sZz0sR8f/yYjqmOe15+3jYJs13jYjXSXZAAPh0mnR/Kn3/q3RD/walp3cNTN++0IxYzayCOFE1M8tGbvh/KPA1YJ/0fclh74h4jreTsq+mSWmhTwM7F2svqaaRmHJzPovtJtCQ3Ob7gyWNamKb3Oc/EPh/wI7p+6YO+08k+TtsG/D3JrYxswrjRNXMLAMR8TQwI337tfR1CcmJUw35avp6FPAbSTsDSOou6bMkJ0utLtH2R5Juk/QxSbmFWUjqnbbNnQh1VzM/ywu8PS1hUkN18/yJt4fs/zN9fSIiZjaxfe45Txdu42VmHYcTVTOz7OR6FXP/L/5lRNQ11CAi/gh8J337CWChpFXAOuAnwPT0tZiuwMeBqcAySeskvZnXthvwWN79m+O36etJTakcEVt5ey5u7vM3dBJVodxzftNgLTOraGp8P2UzM2sL6Qb3i3l7e6Xd0xOXmtL2BOASkiHwLsBLwC3A/5L0un4deCQijsxrMwY4nqQ3di9gCNCbZD/SZ0mSzZsbS5ZLxLMXyVzVDcBOEbGhGW0gmXYwJCLWNqHdaJLPuwkYHhFvNjdeM6sMTlTNzKxVSHqQJAk+KyJK7VzQGs/5T+CbJKdXlTqK1sw6ACeqZmbWKiQdAjwOzAb2acIRqC15Ri/gFaAPsEdEvNrazzCz8uE5qmZm1ioi4h8k81/HkcyFbQufJ9mW6lonqWYdX5esAzAzsw7lUpIe1a5tdP8NJAcSfL+N7m9mZcRD/2ZmZmZWljz0b2ZmZmZlyYmqmZmZmZUlJ6pmZmZmVpacqJqZmZlZWXKiamZmZmZlyYmqmZmZmZUlJ6pmZmZmVpacqJqZmZlZWXKiamZmZmZlyYmqmZmZmZUlJ6pmZmZmVpb+Py8aLl7cYi+TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=120)\n",
    "plt.plot(vds_vals,ids)\n",
    "plt.xlabel(\"Vds (V)\",fontsize=15)\n",
    "plt.ylabel(\"Ids (A)\",fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "be30d2ff-60e0-4ec9-9074-00167a364aea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = MOSFET()\n",
    "plt.figure(dpi=120)\n",
    "for vgs in [0.5,0.9,1.5,2.0,2.5]:\n",
    "    ids = []\n",
    "    vds_vals = [0.01*i for i in range(1,501)]\n",
    "    for v in vds_vals:\n",
    "        ids.append(m.id_vd(vgs=vgs,vds=v,rounding=False))\n",
    "    plt.plot(vds_vals,ids)\n",
    "plt.legend([\"Vgs = \"+str(v) for v in [0.5,0.9,1.5,2.0,2.5]])\n",
    "plt.xlabel(\"Vds (V)\",fontsize=15)\n",
    "plt.ylabel(\"Ids (A)\",fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f82806f-e980-49d1-afde-9b19fb183c7a",
   "metadata": {},
   "source": [
    "## Sub-Vth leakage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1e414fc8-be14-46b7-86d1-67c85b8648f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'No data to train with'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.train_leakage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b1600ccd-9480-4700-9aa8-46f34dcea812",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sub = []\n",
    "for i in range(100):\n",
    "    sub.append(sub_vth(w_l=1e-2,vgs=0.5,vth=1.0))\n",
    "\n",
    "plt.hist(sub,edgecolor='k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "831c2848-f152-4f11-9dfb-949b56a0b674",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sub = []\n",
    "vgs = []\n",
    "for i in range(100):\n",
    "    sub.append(sub_vth(w_l=1e-2,vgs=i*0.01,vth=1.0))\n",
    "    vgs.append(i*0.01)\n",
    "\n",
    "plt.semilogy(vgs,sub)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26c5aed7-bbbd-4048-825a-1ddb482d2ac2",
   "metadata": {},
   "source": [
    "### Synthetic leakage data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e188e247-f6b2-463e-8f2d-556af5afbb21",
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f55a9b8f-0200-482d-a636-d17c03ffd584",
   "metadata": {},
   "outputs": [],
   "source": [
    "w_l_list = [1e-3*i for i in range(1,21)]\n",
    "vgs_list = [0.01*i for i in range(1,91)]\n",
    "vth_list = [0.05*i for i in range(21,31)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "37f31a79-d308-4aee-8441-b2bcaf431b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "comb = list(product(w_l_list,vgs_list,vth_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "70b45002-6271-4dec-b1e2-ffa9e177c51a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict = {'w_l':[],'vgs':[],'vth':[],'sub-vth':[]}\n",
    "for c in comb:\n",
    "    data_dict['w_l'].append(c[0])\n",
    "    data_dict['vgs'].append(c[1])\n",
    "    data_dict['vth'].append(c[2])\n",
    "    data_dict['sub-vth'].append(sub_vth(c[0],c[1],c[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "895de17b-86ce-4717-8620-0645e0e8dcff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data=data_dict,columns=['w_l','vgs','vth','sub-vth'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "015afa45-3f11-4dae-be36-5ca87674cb7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5892</th>\n",
       "      <th>3783</th>\n",
       "      <th>7666</th>\n",
       "      <th>136</th>\n",
       "      <th>5468</th>\n",
       "      <th>16393</th>\n",
       "      <th>5554</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>w_l</th>\n",
       "      <td>7.000000e-03</td>\n",
       "      <td>5.000000e-03</td>\n",
       "      <td>9.000000e-03</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>7.000000e-03</td>\n",
       "      <td>1.900000e-02</td>\n",
       "      <td>7.000000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vgs</th>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>1.900000e-01</td>\n",
       "      <td>4.700000e-01</td>\n",
       "      <td>1.400000e-01</td>\n",
       "      <td>7.000000e-02</td>\n",
       "      <td>2.000000e-01</td>\n",
       "      <td>1.600000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vth</th>\n",
       "      <td>1.150000e+00</td>\n",
       "      <td>1.200000e+00</td>\n",
       "      <td>1.350000e+00</td>\n",
       "      <td>1.350000e+00</td>\n",
       "      <td>1.450000e+00</td>\n",
       "      <td>1.200000e+00</td>\n",
       "      <td>1.250000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub-vth</th>\n",
       "      <td>5.072962e-12</td>\n",
       "      <td>2.015358e-17</td>\n",
       "      <td>4.347818e-15</td>\n",
       "      <td>1.319075e-20</td>\n",
       "      <td>5.576009e-22</td>\n",
       "      <td>2.364681e-16</td>\n",
       "      <td>4.487012e-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                5892          3783          7666          136           5468   \\\n",
       "w_l      7.000000e-03  5.000000e-03  9.000000e-03  1.000000e-03  7.000000e-03   \n",
       "vgs      5.000000e-01  1.900000e-01  4.700000e-01  1.400000e-01  7.000000e-02   \n",
       "vth      1.150000e+00  1.200000e+00  1.350000e+00  1.350000e+00  1.450000e+00   \n",
       "sub-vth  5.072962e-12  2.015358e-17  4.347818e-15  1.319075e-20  5.576009e-22   \n",
       "\n",
       "                16393         5554   \n",
       "w_l      1.900000e-02  7.000000e-03  \n",
       "vgs      2.000000e-01  1.600000e-01  \n",
       "vth      1.200000e+00  1.250000e+00  \n",
       "sub-vth  2.364681e-16  4.487012e-18  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(7).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "782e6934-1a20-48f0-b619-b79ac2f72ebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['log-leakage'] = -np.log10(df['sub-vth'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e610aacd-b4c5-4035-a7e2-24ddd5d74414",
   "metadata": {},
   "source": [
    "## Training leakage model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "044badd8-c358-4dc3-bb8b-8740c55e7d2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Python39\\lib\\site-packages\\keras\\optimizer_v2\\adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(Adam, self).__init__(name, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 177.6796 - mse: 177.6796\n",
      "Epoch 2/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 74.2075 - mse: 74.2075\n",
      "Epoch 3/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 26.5780 - mse: 26.5780\n",
      "Epoch 4/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 15.9009 - mse: 15.9009\n",
      "Epoch 5/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 11.2300 - mse: 11.2300A: 0s - loss: 11.5155\n",
      "Epoch 6/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 8.7529 - mse: 8.7529\n",
      "Epoch 7/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 6.5500 - mse: 6.5500\n",
      "Epoch 8/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 4.3082 - mse: 4.3082\n",
      "Epoch 9/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 2.2950 - mse: 2.2950\n",
      "Epoch 10/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 0.8292 - mse: 0.8292\n",
      "Epoch 11/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 0.1616 - mse: 0.1616\n",
      "Epoch 12/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 0.0502 - mse: 0.0502\n",
      "Epoch 13/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 0.0328 - mse: 0.0328\n",
      "Epoch 14/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 0.0263 - mse: 0.0263\n",
      "Epoch 15/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 0.0235 - mse: 0.0235A - ETA: 0s - loss: 0.0237 - \n",
      "Epoch 16/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 0.0220 - mse: 0.0220\n",
      "Epoch 17/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 0.0211 - mse: 0.0211\n",
      "Epoch 18/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 0.0204 - mse: 0.0204\n",
      "Epoch 19/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 0.0198 - mse: 0.0198\n",
      "Epoch 20/20\n",
      "2520/2520 [==============================] - 3s 1ms/step - loss: 0.0194 - mse: 0.0194A: 1s - loss: 0.0195 - mse: 0.01 - ETA: 1s - lo\n"
     ]
    }
   ],
   "source": [
    "mosfet.train_leakage(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bd7b0ca5-986d-4e81-9e88-652d95a87ee5",
   "metadata": {},
   "outputs": [],
   "source": [
    "m1_trained = m.leakage_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7f41a5ef-8759-4bec-8e7a-830e87590a62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.sqrt(m1_trained.history.history['mse']))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d4cfabc1-314a-4b65-a222-08c30143c964",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1557569958786463e-24"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.leakage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0e71afe1-76d4-4167-be39-c88b49a1aebd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.728817348758802e-15"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.leakage(w_l=1e-1,vgs=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "332077bf-51d3-450d-93e7-44c38242d76f",
   "metadata": {},
   "outputs": [],
   "source": [
    "m._terminals_['gate']=0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "03d11fdf-4ea6-40e9-9c50-812a12523520",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.678188888109029e-13"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.leakage(w_l=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a85b6649-0289-41bd-a402-12d9173aab8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sub = []\n",
    "vgs = []\n",
    "for i in range(100):\n",
    "    sub.append(m.leakage(w_l=1e-2,vgs=i*0.01,vth=1.0))\n",
    "    vgs.append(i*0.01)\n",
    "\n",
    "plt.semilogy(vgs,sub)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8281c32-4f94-48ae-9847-a92b4cabe83e",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = MOSFET(terminals={'source':0.0,\n",
    "            'drain':0.0,\n",
    "            'gate':0.7})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db14d0cf-6941-4fba-b373-1a7dae8971db",
   "metadata": {},
   "outputs": [],
   "source": [
    "m.leakage()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}