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   "metadata": {
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    "tags": []
   },
   "source": [
    "# El transistor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e58a9bef-f1d9-44a2-b7b8-2f24ec1c9f03",
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   "source": [
    "```{admonition} Pàgina en desenvolupament\n",
    "Encara estem treballant en aquest document.   \n",
    "Esperem oferir-vos properament el seu contingut.   \n",
    "Disculpeu les molèsties.\n",
    "```"
   ]
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   "metadata": {
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    },
    "tags": []
   },
   "source": [
    "## El transistor amb emissor comú"
   ]
  },
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    "%%html\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Vb</th>\n",
       "      <th>Vc</th>\n",
       "      <th>Ib</th>\n",
       "      <th>Ic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.900000e-12</td>\n",
       "      <td>9.900000e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.900000e-12</td>\n",
       "      <td>9.900000e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.800000e-12</td>\n",
       "      <td>1.000000e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.3</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.600000e-12</td>\n",
       "      <td>2.060000e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.4</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000e-13</td>\n",
       "      <td>5.300000e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.5</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.440000e-10</td>\n",
       "      <td>2.490000e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.6</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.190000e-08</td>\n",
       "      <td>1.200000e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.7</td>\n",
       "      <td>4.999000</td>\n",
       "      <td>5.549000e-07</td>\n",
       "      <td>5.550000e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.8</td>\n",
       "      <td>4.970000</td>\n",
       "      <td>1.510000e-05</td>\n",
       "      <td>1.500000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.9</td>\n",
       "      <td>4.852000</td>\n",
       "      <td>7.400000e-05</td>\n",
       "      <td>7.400000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.690000</td>\n",
       "      <td>1.549000e-04</td>\n",
       "      <td>1.550000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.1</td>\n",
       "      <td>4.514000</td>\n",
       "      <td>2.432000e-04</td>\n",
       "      <td>2.430000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.2</td>\n",
       "      <td>4.330000</td>\n",
       "      <td>3.349000e-04</td>\n",
       "      <td>3.350000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.3</td>\n",
       "      <td>4.143000</td>\n",
       "      <td>4.286000e-04</td>\n",
       "      <td>4.290000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.4</td>\n",
       "      <td>3.953000</td>\n",
       "      <td>5.234000e-04</td>\n",
       "      <td>5.230000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.5</td>\n",
       "      <td>3.762000</td>\n",
       "      <td>6.191000e-04</td>\n",
       "      <td>6.190000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.6</td>\n",
       "      <td>3.569000</td>\n",
       "      <td>7.153000e-04</td>\n",
       "      <td>7.150000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.7</td>\n",
       "      <td>3.376000</td>\n",
       "      <td>8.120000e-04</td>\n",
       "      <td>8.120000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.8</td>\n",
       "      <td>3.182000</td>\n",
       "      <td>9.091000e-04</td>\n",
       "      <td>9.090000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.9</td>\n",
       "      <td>2.987000</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>1.006000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.792000</td>\n",
       "      <td>1.100000e-03</td>\n",
       "      <td>1.104000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.1</td>\n",
       "      <td>2.596000</td>\n",
       "      <td>1.200000e-03</td>\n",
       "      <td>1.202000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.2</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>1.300000e-03</td>\n",
       "      <td>1.300000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2.3</td>\n",
       "      <td>2.204000</td>\n",
       "      <td>1.400000e-03</td>\n",
       "      <td>1.398000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.4</td>\n",
       "      <td>2.008000</td>\n",
       "      <td>1.500000e-03</td>\n",
       "      <td>1.496000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2.5</td>\n",
       "      <td>1.811000</td>\n",
       "      <td>1.600000e-03</td>\n",
       "      <td>1.595000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2.6</td>\n",
       "      <td>1.614000</td>\n",
       "      <td>1.700000e-03</td>\n",
       "      <td>1.693000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2.7</td>\n",
       "      <td>1.417000</td>\n",
       "      <td>1.800000e-03</td>\n",
       "      <td>1.792000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.8</td>\n",
       "      <td>1.220000</td>\n",
       "      <td>1.900000e-03</td>\n",
       "      <td>1.890000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2.9</td>\n",
       "      <td>1.022000</td>\n",
       "      <td>2.000000e-03</td>\n",
       "      <td>1.989000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.824765</td>\n",
       "      <td>2.100000e-03</td>\n",
       "      <td>2.088000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3.1</td>\n",
       "      <td>0.627158</td>\n",
       "      <td>2.200000e-03</td>\n",
       "      <td>2.186000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3.2</td>\n",
       "      <td>0.429473</td>\n",
       "      <td>2.300000e-03</td>\n",
       "      <td>2.285000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3.3</td>\n",
       "      <td>0.255856</td>\n",
       "      <td>2.400000e-03</td>\n",
       "      <td>2.372000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3.4</td>\n",
       "      <td>0.205486</td>\n",
       "      <td>2.500000e-03</td>\n",
       "      <td>2.397000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3.5</td>\n",
       "      <td>0.187098</td>\n",
       "      <td>2.600000e-03</td>\n",
       "      <td>2.406000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3.6</td>\n",
       "      <td>0.176139</td>\n",
       "      <td>2.700000e-03</td>\n",
       "      <td>2.412000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>3.7</td>\n",
       "      <td>0.168376</td>\n",
       "      <td>2.800000e-03</td>\n",
       "      <td>2.416000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3.8</td>\n",
       "      <td>0.162379</td>\n",
       "      <td>2.900000e-03</td>\n",
       "      <td>2.419000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3.9</td>\n",
       "      <td>0.157500</td>\n",
       "      <td>3.000000e-03</td>\n",
       "      <td>2.421000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.153392</td>\n",
       "      <td>3.100000e-03</td>\n",
       "      <td>2.423000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4.1</td>\n",
       "      <td>0.149845</td>\n",
       "      <td>3.200000e-03</td>\n",
       "      <td>2.425000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4.2</td>\n",
       "      <td>0.146727</td>\n",
       "      <td>3.300000e-03</td>\n",
       "      <td>2.427000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>4.3</td>\n",
       "      <td>0.143946</td>\n",
       "      <td>3.400000e-03</td>\n",
       "      <td>2.428000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>4.4</td>\n",
       "      <td>0.141437</td>\n",
       "      <td>3.500000e-03</td>\n",
       "      <td>2.429000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4.5</td>\n",
       "      <td>0.139151</td>\n",
       "      <td>3.600000e-03</td>\n",
       "      <td>2.430000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>4.6</td>\n",
       "      <td>0.137054</td>\n",
       "      <td>3.700000e-03</td>\n",
       "      <td>2.431000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4.7</td>\n",
       "      <td>0.135116</td>\n",
       "      <td>3.800000e-03</td>\n",
       "      <td>2.432000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>4.8</td>\n",
       "      <td>0.133316</td>\n",
       "      <td>3.900000e-03</td>\n",
       "      <td>2.433000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4.9</td>\n",
       "      <td>0.131634</td>\n",
       "      <td>4.000000e-03</td>\n",
       "      <td>2.434000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.130058</td>\n",
       "      <td>4.100000e-03</td>\n",
       "      <td>2.435000e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Vb        Vc            Ib            Ic\n",
       "0   0.0  5.000000  4.900000e-12  9.900000e-12\n",
       "1   0.1  5.000000  4.900000e-12  9.900000e-12\n",
       "2   0.2  5.000000  4.800000e-12  1.000000e-11\n",
       "3   0.3  5.000000  4.600000e-12  2.060000e-11\n",
       "4   0.4  5.000000  6.000000e-13  5.300000e-10\n",
       "5   0.5  5.000000  2.440000e-10  2.490000e-08\n",
       "6   0.6  5.000000  1.190000e-08  1.200000e-06\n",
       "7   0.7  4.999000  5.549000e-07  5.550000e-05\n",
       "8   0.8  4.970000  1.510000e-05  1.500000e-03\n",
       "9   0.9  4.852000  7.400000e-05  7.400000e-03\n",
       "10  1.0  4.690000  1.549000e-04  1.550000e-02\n",
       "11  1.1  4.514000  2.432000e-04  2.430000e-02\n",
       "12  1.2  4.330000  3.349000e-04  3.350000e-02\n",
       "13  1.3  4.143000  4.286000e-04  4.290000e-02\n",
       "14  1.4  3.953000  5.234000e-04  5.230000e-02\n",
       "15  1.5  3.762000  6.191000e-04  6.190000e-02\n",
       "16  1.6  3.569000  7.153000e-04  7.150000e-02\n",
       "17  1.7  3.376000  8.120000e-04  8.120000e-02\n",
       "18  1.8  3.182000  9.091000e-04  9.090000e-02\n",
       "19  1.9  2.987000  1.000000e-03  1.006000e-01\n",
       "20  2.0  2.792000  1.100000e-03  1.104000e-01\n",
       "21  2.1  2.596000  1.200000e-03  1.202000e-01\n",
       "22  2.2  2.400000  1.300000e-03  1.300000e-01\n",
       "23  2.3  2.204000  1.400000e-03  1.398000e-01\n",
       "24  2.4  2.008000  1.500000e-03  1.496000e-01\n",
       "25  2.5  1.811000  1.600000e-03  1.595000e-01\n",
       "26  2.6  1.614000  1.700000e-03  1.693000e-01\n",
       "27  2.7  1.417000  1.800000e-03  1.792000e-01\n",
       "28  2.8  1.220000  1.900000e-03  1.890000e-01\n",
       "29  2.9  1.022000  2.000000e-03  1.989000e-01\n",
       "30  3.0  0.824765  2.100000e-03  2.088000e-01\n",
       "31  3.1  0.627158  2.200000e-03  2.186000e-01\n",
       "32  3.2  0.429473  2.300000e-03  2.285000e-01\n",
       "33  3.3  0.255856  2.400000e-03  2.372000e-01\n",
       "34  3.4  0.205486  2.500000e-03  2.397000e-01\n",
       "35  3.5  0.187098  2.600000e-03  2.406000e-01\n",
       "36  3.6  0.176139  2.700000e-03  2.412000e-01\n",
       "37  3.7  0.168376  2.800000e-03  2.416000e-01\n",
       "38  3.8  0.162379  2.900000e-03  2.419000e-01\n",
       "39  3.9  0.157500  3.000000e-03  2.421000e-01\n",
       "40  4.0  0.153392  3.100000e-03  2.423000e-01\n",
       "41  4.1  0.149845  3.200000e-03  2.425000e-01\n",
       "42  4.2  0.146727  3.300000e-03  2.427000e-01\n",
       "43  4.3  0.143946  3.400000e-03  2.428000e-01\n",
       "44  4.4  0.141437  3.500000e-03  2.429000e-01\n",
       "45  4.5  0.139151  3.600000e-03  2.430000e-01\n",
       "46  4.6  0.137054  3.700000e-03  2.431000e-01\n",
       "47  4.7  0.135116  3.800000e-03  2.432000e-01\n",
       "48  4.8  0.133316  3.900000e-03  2.433000e-01\n",
       "49  4.9  0.131634  4.000000e-03  2.434000e-01\n",
       "50  5.0  0.130058  4.100000e-03  2.435000e-01"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dades = pd.read_csv('data/Control/q1.csv')\n",
    "dades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6bc94fab-abe6-4f28-a5dd-f6cddf9e4884",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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m/AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "Vb=dades['Vb']\n",
    "Vc=dades['Vc']\n",
    "Ib=dades['Ib']*1000\n",
    "Ic=dades['Ic']\n",
    "%matplotlib inline                               \n",
    "plt.rcParams['figure.figsize'] = (16,7)           # determines the dimensions (size of the figure)\n",
    "fig = plt.figure()\n",
    "plt.plot(Vb, Ic)\n",
    "plt.xlabel('$V_B \\ [V]$')\n",
    "plt.ylabel('$I_c \\ [A]$')\n",
    "plt.text(0.1,0.2,'Zona de tall')\n",
    "plt.axvline(x=0.80, color='red')\n",
    "plt.text(1.9,0.2,'Zona activa')\n",
    "plt.axvline(x=3.3, color='red')\n",
    "plt.text(4,0.2,'Zona de saturació')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19d21756-0096-4860-823e-da72d706b09e",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## El transistor en zona activa. Electrònica analògica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "06c10b39-685f-4672-821c-7cf1c01aaacd",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline                               \n",
    "plt.rcParams['figure.figsize'] = (16,7)           # determines the dimensions (size of the figure)\n",
    "fig = plt.figure()\n",
    "plt.plot(Ib, Ic)\n",
    "plt.xlabel('$I_B \\ [mA]$')\n",
    "plt.ylabel('$I_C \\ [A]$')\n",
    "#plt.text(0.1,3.5,'Zona de tall')\n",
    "#plt.axvline(x=0.80, color='red')\n",
    "plt.text(0.7,0.2,'Zona activa')\n",
    "plt.axvline(x=2.4, color='red')\n",
    "plt.text(3,0.2,'Zona de saturació')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50eb2cba-b9cd-4d47-800a-3a718b291cef",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## El transistor en tall / saturació. Electrònica digital"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c6dff479-c906-4412-880c-31f199c85c84",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline  \n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = (16,7)           # determines the dimensions (size of the figure)\n",
    "fig = plt.figure()\n",
    "plt.plot(Vb, Vc)\n",
    "plt.xlabel('$V_B \\ [V]$')\n",
    "plt.ylabel('$V_c \\ [V]$')\n",
    "plt.text(0.1,3.5,'Zona de tall')\n",
    "plt.axhline(y=0.3, color='green')\n",
    "plt.axvline(x=0.80, color='red')\n",
    "plt.text(1.9,3.5,'Zona activa')\n",
    "plt.axvline(x=3.3, color='red')\n",
    "plt.text(4,3.5,'Zona de saturació')\n",
    "plt.axhline(y=4.9, color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fbe7396-8f23-4184-90b5-e3a352e0cc37",
   "metadata": {},
   "source": [
    "### Circuits de conmutació. El multivibrador astable.\n",
    "\n",
    "Podem aprofitar que la càrrega i descarrega d'un condensador no és instantània, ja que depèn de la resistència per la qual agafa o deixa anar electrons, per retardar el canvi tall / saturació del transistor. Apareix així un temps característic $\\tau = R C$.\n",
    "\n",
    "Un circuit típic que aprofita aquest recurs és el multivibrador astable {cite}`Multivibrador_astable_a_transistores` :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "54e708d0-1758-46c1-b753-e7da96f05d42",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       " <iframe src=\"https://www.falstad.com/circuit/circuitjs.html?ctz=CQAgjCAMB0l3BWEAOaAmZYCcB2BzkcBmLUhJBSECqhAUwFowwAoAd3DDRABZkQ0kfnyjsBQ3vwzD+kMdMkgiyHorkAnTtxHNuYHADYoSohvEzz4Q8bCRBLTQpEL9Rqrfublqkd6tulewBjLX9LV2MYeEgwfgYYHhiyAzQcNCwwIgMcHChYGJYQvwiXayoo+FilaGzyHgNkBBwsfFrVCtYOYus-NETRABcBAgEEI16xmxAGImgMykaeMCX8HEgiXISeBFsUwRxYsAQMcDgQABM6ADMAQwBXABsB+QkSkbRJuQ5So0EpT5YQz+oyMuhBUxgBhWRCWyBhGRIbhqKihPByBgymEwejOl1uj2eHDBETBfXKLAASqERFC1MZ+khynkECwAOahVJ6LhKSDtUTs3ppJQqHl8uQAD3ACCwAm5sSoaCOvHARgAwqxJUR9LKiCYpGAjKoDSBVWgWJLGgIDO5ILk0AZdUajABFDVKLAK6W8bn27QqkDOs2JXXYbjAtB9AT9dxoAD6HwMscgsaWdljCCT0DAsaY8djXFjRBYwYEQuBplUZJs2YTSZTHnTmZ4OZr+ezRZLREE4BORGOKqZyHzGDrqbjGYSLbzQ4763APCNJxJ1uMOGHQ+TY8bMGzubja6LAHtwLraYbIKQUNAVBeEFqI7ysIRZPkYsYcSADCwgA\" width=\"800\" height=\"600\"></iframe>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    " <iframe src=\"https://www.falstad.com/circuit/circuitjs.html?ctz=CQAgjCAMB0l3BWEAOaAmZYCcB2BzkcBmLUhJBSECqhAUwFowwAoAd3DDRABZkQ0kfnyjsBQ3vwzD+kMdMkgiyHorkAnTtxHNuYHADYoSohvEzz4Q8bCRBLTQpEL9Rqrfublqkd6tulewBjLX9LV2MYeEgwfgYYHhiyAzQcNCwwIgMcHChYGJYQvwiXayoo+FilaGzyHgNkBBwsfFrVCtYOYus-NETRABcBAgEEI16xmxAGImgMykaeMCX8HEgiXISeBFsUwRxYsAQMcDgQABM6ADMAQwBXABsB+QkSkbRJuQ5So0EpT5YQz+oyMuhBUxgBhWRCWyBhGRIbhqKihPByBgymEwejOl1uj2eHDBETBfXKLAASqERFC1MZ+khynkECwAOahVJ6LhKSDtUTs3ppJQqHl8uQAD3ACCwAm5sSoaCOvHARgAwqxJUR9LKiCYpGAjKoDSBVWgWJLGgIDO5ILk0AZdUajABFDVKLAK6W8bn27QqkDOs2JXXYbjAtB9AT9dxoAD6HwMscgsaWdljCCT0DAsaY8djXFjRBYwYEQuBplUZJs2YTSZTHnTmZ4OZr+ezRZLREE4BORGOKqZyHzGDrqbjGYSLbzQ4763APCNJxJ1uMOGHQ+TY8bMGzubja6LAHtwLraYbIKQUNAVBeEFqI7ysIRZPkYsYcSADCwgA\" width=\"800\" height=\"600\"></iframe>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcf6404f-5ec5-4b31-bcd6-902f3a91c9b6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Productes comercials\n",
    "\n",
    "Un transistor molt utilitzat és el [BC337](http://www.jorts.net/jupbook/pdf/BC337.pdf)\n",
    "\n",
    "![](img/Control/bc337.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf8ed274-97e2-418b-825c-a91c5aff1d3d",
   "metadata": {},
   "source": [
    "## Per a aprofundir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2ed33d40-a914-4068-a398-42fe3875bfe2",
   "metadata": {
    "editable": true,
    "raw_mimetype": "",
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/Eyrn2IShE90\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7fc537e665f0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('Eyrn2IShE90')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a846fbec-f3a8-49e1-93ab-02c5c7bfbb91",
   "metadata": {
    "editable": true,
    "raw_mimetype": "",
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/FVnXEBCAVJo\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7fc536d25960>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('FVnXEBCAVJo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "43b1cea1-4356-4688-9e2c-036cd1fab89b",
   "metadata": {
    "editable": true,
    "raw_mimetype": "",
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/qHovTt8lCOI\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7fc536d256f0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('qHovTt8lCOI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "955db71c-864f-46bf-a946-1896d9266fb7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venvPy3",
   "language": "python",
   "name": "venvpy3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}