Adding comparison against simulation for nfet

.gitmodules

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+[submodule "technology/skywater-pdk-libs-sky130_fd_pr"]
+	path = technology/skywater-pdk-libs-sky130_fd_pr
+	url = https://github.com/google/skywater-pdk-libs-sky130_fd_pr.git

notebooks/README.md

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+## Comapring simulated data with raw data 
+
+Result of comparison done in "sky130_plot_simdata_compare.ipynb" notebook indicates that simulated data is almost equal to raw data except for loc=5290 
+

notebooks/raw_gen.py

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+import pathlib
+import DMT.core
+import pandas as pd
+import glob
+from subprocess import check_call
+from docopt import docopt
+
+# reading raw data files
+curve_type = ["ID_VDS", "ID_VGS"]
+cells_dir = pathlib.Path("../sky130_fd_pr/cells")
+cell_dir = cells_dir / "nfet_01v8"
+data_files = glob.glob(f"{cell_dir}/*.mdm")
+
+sel_files_vd = dict() 
+sel_files_vg = dict() 
+
+for file in data_files:
+
+    part1 = file.split("(")[1]
+    part2 = part1.split(")")[0]
+    data = part2.split("_")
+
+    if "D3" in data:
+        loc = int(data[0])
+        s_pin = 0
+        d_pin = int(data[1])
+    else:
+        loc = int(data[0])
+        s_pin = int(data[1])
+        d_pin = int(data[2])
+
+    if "IDVD" in file:
+        sel_files_vd[file] = ((loc, s_pin, d_pin))
+    elif "IDVG" in file:
+        sel_files_vg[file] = ((loc, s_pin, d_pin))
+
+total_sel_files = [sel_files_vd, sel_files_vg]
+for sel_files in total_sel_files:
+    index = 1
+    raw_df = pd.DataFrame()
+    for mdm_path, name in sel_files.items():
+        mdm_path = pathlib.Path(mdm_path)
+        measurement = DMT.core.DutMeas(
+            database_dir=None,
+            dut_type=DMT.core.DutType.device,
+            name=mdm_path.stem,
+            reference_node="E",
+        )
+
+        measurement.add_data(pathlib.Path(mdm_path), key=mdm_path.stem)
+        df_read = measurement.data[mdm_path.stem]
+        df_read["VGS"] = df_read["VG"] - df_read["VS"]
+        df_read["VSB"] = df_read["VS"] - df_read["VB"]
+        df_read["VDS"] = df_read["VD"] - df_read["VS"]
+        df_read.drop(columns=["VG", "VS", "VD", "VB"], inplace=True)
+        df_read["loc"] = name[0]
+        df_read["s_pin"] = name[1]
+        df_read["d_pin"] = name[2]
+
+        if index == 1:
+            raw_df = df_read
+        else:
+            raw_df = pd.concat([raw_df, df_read])
+
+        index += 1
+
+    if sel_files == sel_files_vd:
+        raw_df.to_csv(f"{cell_dir}/raw_data_idvd.csv")
+    elif sel_files == sel_files_vg:
+        raw_df.to_csv(f"{cell_dir}/raw_data_idvg.csv")

notebooks/sky130_plot_simdata_compare.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import ipywidgets as widgets\n",
+    "from matplotlib.ticker import EngFormatter\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "import matplotlib as mpl\n",
+    "import re\n",
+    "import numpy as np\n",
+    "from jinja2 import Template\n",
+    "from subprocess import check_call\n",
+    "import concurrent.futures\n",
+    "import multiprocessing as mp\n",
+    "import glob\n",
+    "import pathlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reading Pad Documentation csv file to extract parameters\n",
+    "doc_df = pd.read_csv(\"pad_documentation/Pad_documentation _for _SKY130_ MPW_Manufacturing.csv\")\n",
+    "nfet_01v8_df = doc_df.loc[doc_df[\"New Style Name\"] == \"nfet_01v8\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# curve_type must be one of ['ID_VDS', 'ID_VGS']\n",
+    "curve_type = \"ID_VDS\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generating sim data\n",
+    "def simulate_device(netlist_path):\n",
+    "    check_call(f\"ngspice -b {netlist_path} -o output.log  \", shell= True)\n",
+    "\n",
+    "workers_count = 2 * mp.cpu_count()\n",
+    "nmos_netlist = \"templates/nfet_01v8_template.cir\"\n",
+    "loc_sweep = list()\n",
+    "s_pin_sweep = list()\n",
+    "d_pin_sweep = list()\n",
+    "\n",
+    "if curve_type == \"ID_VDS\":\n",
+    "    sim_cmd = \"DC vds 0 1.8 0.05\"\n",
+    "    sweep_range = \"start=0 stop=1.8 step=0.36\"\n",
+    "    v_sweep = \"vgs\"\n",
+    "\n",
+    "elif curve_type == \"ID_VGS\":\n",
+    "    sim_cmd = \"DC vgs 0 1.8 0.05\"\n",
+    "    sweep_range = \"start=0.1 stop=1.8 step=1.7\"\n",
+    "    v_sweep = \"vds\"\n",
+    "\n",
+    "with concurrent.futures.ProcessPoolExecutor(max_workers=workers_count) as executor:\n",
+    "    for i, row in nfet_01v8_df.iterrows():\n",
+    "        sim_data = row[\"Description\"]\n",
+    "        subs = [';', 'nshort', 'in DNW', 'contact-gate=\\d+.\\d+um']\n",
+    "        for element in subs:\n",
+    "            sim_data = re.sub(element, ' ' ,sim_data)\n",
+    "\n",
+    "        # get pins loc for each file\n",
+    "        pin1 = int()\n",
+    "        pin2 = int()\n",
+    "        for c in row.index:\n",
+    "            if row[c] == \"s\":\n",
+    "                pin1 = int(re.sub(\"Pin \", '', c))\n",
+    "            elif row[c] == \"d\":\n",
+    "                pin2 = int(re.sub(\"Pin \", '', c))\n",
+    "\n",
+    "        # saving sweep data\n",
+    "        s_pin_sweep.append(int(pin1))\n",
+    "        d_pin_sweep.append(int(pin2))\n",
+    "        if row[\"Mod #\"] == row[\"Mod #\"]:\n",
+    "            loc_sweep.append(int(row[\"Mod #\"]))\n",
+    "        \n",
+    "        # get width, length, multiplier\n",
+    "        width = re.findall(\"w=\\d*.\\d*\", sim_data)[0]\n",
+    "        width = re.sub(\"=\", \"\", width)\n",
+    "        length = re.findall(\"l=\\d*.\\d*\", sim_data)[0]\n",
+    "        length = re.sub(\"=\", \"\", length)\n",
+    "        multiplier = re.findall(\"m=\\d*\", sim_data)[0]\n",
+    "        multiplier = re.sub(\"=\", \"\", multiplier)\n",
+    "\n",
+    "        # naming circuit, csv files\n",
+    "        file_name = f\"nfet_01v8_{loc_sweep[-1]}_{pin1}_{pin2}\"\n",
+    "        netlist_path = f\"{file_name}.cir\"\n",
+    "        csv_path = f\"{file_name}.csv\"\n",
+    "\n",
+    "        with open(nmos_netlist) as f:\n",
+    "                    tmpl = Template(f.read())\n",
+    "                    with open(netlist_path, \"w\") as netlist:\n",
+    "                        netlist.write(\n",
+    "                            tmpl.render(\n",
+    "                                csv_path=csv_path,\n",
+    "                                dimensions=sim_data,\n",
+    "                                sim_cmd=sim_cmd,\n",
+    "                                sweep_range=sweep_range,\n",
+    "                                v_sweep=v_sweep,\n",
+    "                            )\n",
+    "                        )\n",
+    "        \n",
+    "        executor.submit(simulate_device, netlist_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clean unused files\n",
+    "check_call(\"rm -f nfet_*.cir\", shell=True)\n",
+    "check_call(\"rm -f output.log\", shell=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# reading raw data generated from \"raw_gen.py\" script\n",
+    "data_dir = pathlib.Path(\"../sky130_fd_pr/cells\")\n",
+    "data_dir = data_dir / \"nfet_01v8\"\n",
+    "if curve_type == \"ID_VDS\":\n",
+    "    raw_df = pd.read_csv(f\"{data_dir}/raw_data_idvd.csv\")\n",
+    "elif curve_type == \"ID_VGS\":\n",
+    "    raw_df = pd.read_csv(f\"{data_dir}/raw_data_idvg.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# widgets settings \n",
+    "loc_sweep = [*set(loc_sweep)]\n",
+    "s_pin_sweep = [*set(s_pin_sweep)]\n",
+    "d_pin_sweep = [*set(d_pin_sweep)]\n",
+    "\n",
+    "loc = widgets.Dropdown(\n",
+    "    options=loc_sweep,\n",
+    "    value=loc_sweep[0],\n",
+    "    description='loc:',\n",
+    "    disabled=False,\n",
+    ")\n",
+    "s_pin = widgets.Dropdown(\n",
+    "    options=s_pin_sweep,\n",
+    "    value=s_pin_sweep[0],\n",
+    "    description='s-pin:',\n",
+    "    disabled=False,\n",
+    ")\n",
+    "d_pin = widgets.Dropdown(\n",
+    "    options=d_pin_sweep,\n",
+    "    value=d_pin_sweep[0],\n",
+    "    description='d-pin:',\n",
+    "    disabled=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "blue_range =  ListedColormap(mpl.cm.Blues(np.linspace(0.3, 0.8, 256)))\n",
+    "red_range = ListedColormap(mpl.cm.Reds(np.linspace(0.3, 0.8, 256)))\n",
+    "\n",
+    "sim_files = glob.glob(f\"nfet_01v8*.csv\")\n",
+    "def get_sim_df(loc, s_pin, d_pin):\n",
+    "    csv_file = str()\n",
+    "    for file in sim_files:\n",
+    "        file_sp = file.split(\"_\")\n",
+    "        d_read = int(file_sp[4].split(\".\")[0])\n",
+    "        \n",
+    "        if loc == int(file_sp[2]) and s_pin == int(file_sp[3]) and d_pin == d_read:\n",
+    "            csv_file = file\n",
+    "            break\n",
+    "    if csv_file != \"\":\n",
+    "        sim_df = pd.read_csv(csv_file, delimiter=r\"\\s+\") \n",
+    "        sim_df.drop(\n",
+    "                   sim_df.columns[[x for x in range(0, sim_df.shape[1]) if x % 2 == 0 and x != 0]],\n",
+    "                   inplace=True,\n",
+    "                   axis=1,\n",
+    "                )\n",
+    "        sim_df.columns = [\n",
+    "            \"VDS\",\n",
+    "            \"VGS\",\n",
+    "            \"VSB\",\n",
+    "            \"ID\",\n",
+    "            \"GM\",\n",
+    "        ]\n",
+    "    else:\n",
+    "        raise(Exception(\"There is no data for selected loc, s-pin, d-pin\"))\n",
+    "\n",
+    "    return sim_df\n",
+    "\n",
+    "# plotting data:\n",
+    "def plot(loc, s_pin, d_pin):\n",
+    "    tdf_sim= get_sim_df(loc, s_pin, d_pin)\n",
+    "    tdf_raw = raw_df.loc[(raw_df[\"loc\"] == loc) &  (raw_df[\"s_pin\"] == s_pin)\n",
+    "                 & (raw_df[\"d_pin\"] == d_pin)]\n",
+    "    vsb_values = [*set(tdf_raw[\"VSB\"].tolist())]\n",
+    "    \n",
+    "    for VSB in vsb_values:\n",
+    "        tdf_sim1 = tdf_sim.loc[(tdf_sim[\"VSB\"] == VSB)]\n",
+    "        tdf_raw1 = tdf_raw.loc[(tdf_raw[\"VSB\"] == VSB)]\n",
+    "        id_df_sim = tdf_sim1[[\"VDS\", \"VGS\", \"ID\"]].copy()\n",
+    "        id_df_raw = tdf_raw1[[\"VDS\", \"VGS\", \"ID\"]].copy()\n",
+    "\n",
+    "        if curve_type == \"ID_VDS\":\n",
+    "            id_df_sim.set_index(\"VDS\", inplace=True)\n",
+    "            id_df_sim.sort_index(inplace=True)\n",
+    "            id_df_raw.set_index(\"VDS\", inplace=True)\n",
+    "            id_df_raw.sort_index(inplace=True)\n",
+    "            id_df_sim = pd.pivot_table(id_df_sim.reset_index(),\n",
+    "                                       values=\"ID\",\n",
+    "                                       index=\"VDS\",\n",
+    "                                       columns=[\"VGS\"])\n",
+    "            ax = id_df_sim.plot(colormap=blue_range, grid=True, figsize=(12,6))\n",
+    "            id_df_raw = pd.pivot_table(id_df_raw.reset_index(),\n",
+    "                                       values=\"ID\",\n",
+    "                                       index=\"VDS\",\n",
+    "                                       columns=[\"VGS\"])\n",
+    "            id_df_raw.plot(colormap=red_range, ax=ax)\n",
+    "            plt.legend(title= \"VGS\", loc=0)\n",
+    "            volt_formatter = EngFormatter(unit='V')\n",
+    "            amp_formatter = EngFormatter(unit='A')\n",
+    "            ax.xaxis.set_major_formatter(volt_formatter)\n",
+    "            ax.yaxis.set_major_formatter(amp_formatter)\n",
+    "            plt.title(f\"IDS vs VDS at VSB={VSB} - Simulated (Blues) - Measured (Reds)\")\n",
+    "        elif curve_type == \"ID_VGS\":\n",
+    "            id_df_sim.set_index(\"VGS\", inplace=True)\n",
+    "            id_df_sim.sort_index(inplace=True)\n",
+    "            id_df_raw.set_index(\"VGS\", inplace=True)\n",
+    "            id_df_raw.sort_index(inplace=True)\n",
+    "            id_df_sim = pd.pivot_table(id_df_sim.reset_index(),\n",
+    "                                       values=\"ID\",\n",
+    "                                       index=\"VGS\",\n",
+    "                                       columns=[\"VDS\"])\n",
+    "            ax = id_df_sim.plot(colormap=blue_range, grid=True, figsize=(12,6))\n",
+    "            id_df_raw = pd.pivot_table(id_df_raw.reset_index(),\n",
+    "                                       values=\"ID\",\n",
+    "                                       index=\"VGS\",\n",
+    "                                       columns=[\"VDS\"])\n",
+    "            id_df_raw.plot(colormap=red_range, ax=ax)\n",
+    "            plt.legend(title= \"VDS\", loc=0)\n",
+    "            volt_formatter = EngFormatter(unit='V')\n",
+    "            amp_formatter = EngFormatter(unit='A')\n",
+    "            ax.xaxis.set_major_formatter(volt_formatter)\n",
+    "            ax.yaxis.set_major_formatter(amp_formatter)\n",
+    "            plt.title(f\"IDS vs VGS VSB={VSB} - Simulated (Blues) - Measured (Reds)\")\n",
+    "        plt.ylabel(\"IDS\")\n",
+    "    \n",
+    "    \n",
+    "# interactive mode\n",
+    "widgets.interact(plot, loc=loc, s_pin=s_pin, d_pin=d_pin)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
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+ },
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+ "nbformat_minor": 2
+}