| # Copyright 2022 Efabless Corporation |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| """ |
| Usage: |
| models_regression.py [--num_cores=<num>] |
| |
| -h, --help Show help text. |
| -v, --version Show version. |
| --num_cores=<num> Number of cores to be used by simulator |
| """ |
| |
| from re import T |
| from docopt import docopt |
| import pandas as pd |
| import numpy as np |
| import os |
| from jinja2 import Template |
| import concurrent.futures |
| import shutil |
| import warnings |
| warnings.simplefilter(action='ignore', category=FutureWarning) |
| |
| def call_simulator(file_name): |
| """Call simulation commands to perform simulation. |
| Args: |
| file_name (str): Netlist file name. |
| """ |
| os.system(f"ngspice -b -a {file_name} -o {file_name}.log > {file_name}.log") |
| |
| def ext_measured(device,vn,d_in, Id_sim, corner): |
| |
| # Get dimensions used for each device |
| dirpath = f"{device}_{Id_sim}_{corner}" |
| dimensions = pd.read_csv(f"{dirpath}/{device}.csv",usecols=["W (um)" , "L (um)"]) |
| loops = dimensions["L (um)"].count() |
| |
| # Extracting measured values for each W & L |
| for i in range (0,loops): |
| width = dimensions["W (um)"].iloc[i] |
| length = dimensions["L (um)"].iloc[i] |
| |
| # Special case for 1st measured values |
| if i == 0 : |
| # measured Id |
| col_list = [f"{vn}",f"{d_in}_{corner}"] |
| df_measured = pd.read_csv(f"{dirpath}/{device}.csv",usecols=col_list) |
| df_measured.columns = [f"{vn}",f"{d_in}_{corner}"] |
| df_measured.to_csv(f"{dirpath}/measured_{Id_sim}/{i}_measured_A{width}_P{length}.csv", index = False) |
| else: |
| # measured Id |
| col_list = [f"{vn}",f"{d_in}_{corner}.{i}"] |
| df_measured = pd.read_csv(f"{dirpath}/{device}.csv",usecols=col_list) |
| df_measured.columns = [f"{vn}",f"{d_in}_{corner}"] |
| df_measured.to_csv(f"{dirpath}/measured_{Id_sim}/{i}_measured_A{width}_P{length}.csv", index = False) |
| |
| def ext_simulated(device,vn,d_in,vn_sweeps,Id_sim, corner): |
| |
| # Get dimensions used for each device |
| dirpath = f"{device}_{Id_sim}_{corner}" |
| dimensions = pd.read_csv(f"{dirpath}/{device}.csv",usecols=["W (um)" , "L (um)"]) |
| loops = dimensions["L (um)"].count() |
| netlist_tmp = f"./device_netlists/{Id_sim}.spice" |
| for i in range (0,loops): |
| width = dimensions["W (um)"].iloc[int(i)] |
| length = dimensions["L (um)"].iloc[int(i)] |
| |
| if i % 4 == 0: temp = -40 |
| elif i % 4 == 1: temp = 25 |
| elif i % 4 == 2: temp = 125 |
| else: |
| temp = 175 |
| with open(netlist_tmp) as f: |
| tmpl = Template(f.read()) |
| os.makedirs(f"{dirpath}/{device}_netlists_{Id_sim}",exist_ok=True) |
| with open(f"{dirpath}/{device}_netlists_{Id_sim}/{i}_{device}_netlist_A{width}_P{length}.spice", "w") as netlist: |
| netlist.write(tmpl.render(device = device, area = width, pj = length, i = i , temp = temp, Id_sim = Id_sim , corner = corner )) |
| netlist_path = f"{dirpath}/{device}_netlists_{Id_sim}/{i}_{device}_netlist_A{width}_P{length}.spice" |
| # Running ngspice for each netlist |
| with concurrent.futures.ProcessPoolExecutor(max_workers=workers_count) as executor: |
| executor.submit(call_simulator, netlist_path) |
| |
| # Writing simulated data |
| df_simulated = pd.read_csv(f"{dirpath}/simulated_{Id_sim}/{i}_simulated_A{width}_P{length}.csv",header=None, delimiter=r"\s+") |
| df_simulated.to_csv(f"{dirpath}/simulated_{Id_sim}/{i}_simulated_A{width}_P{length}.csv",index= False) |
| |
| # empty array to append in it shaped (vn_sweeps, number of trials + 1) |
| new_array = np.empty((vn_sweeps, 1+int(df_simulated.shape[0]/vn_sweeps))) |
| new_array[:, 0] = df_simulated.iloc[:vn_sweeps, 0] |
| times = int(df_simulated.shape[0]/vn_sweeps) |
| |
| for j in range(times): |
| new_array[:, (j+1)] = df_simulated.iloc[j*vn_sweeps:(j+1)*vn_sweeps, 1] |
| |
| # Writing final simulated data |
| df_simulated = pd.DataFrame(new_array) |
| df_simulated.to_csv(f"{dirpath}/simulated_{Id_sim}/{i}_simulated_A{width}_P{length}.csv",index= False) |
| df_simulated.columns = [f"{vn}",f"{d_in}_{corner}"] |
| df_simulated.to_csv(f"{dirpath}/simulated_{Id_sim}/{i}_simulated_A{width}_P{length}.csv",index= False) |
| |
| def error_cal(device,vn,d_in,Id_sim, corner): |
| |
| # Get dimensions used for each device |
| dirpath = f"{device}_{Id_sim}_{corner}" |
| dimensions = pd.read_csv(f"{dirpath}/{device}.csv",usecols=["W (um)" , "L (um)"]) |
| loops = dimensions["L (um)"].count() |
| df_final = pd.DataFrame() |
| for i in range (0,loops): |
| width = dimensions["W (um)"].iloc[int(i)] |
| length = dimensions["L (um)"].iloc[int(i)] |
| if i % 4 == 0: temp = -40 |
| elif i % 4 == 1: temp = 25 |
| elif i % 4 == 2: temp = 125 |
| else: |
| temp = 175 |
| |
| measured = pd.read_csv(f"{dirpath}/measured_{Id_sim}/{i}_measured_A{width}_P{length}.csv") |
| simulated = pd.read_csv(f"{dirpath}/simulated_{Id_sim}/{i}_simulated_A{width}_P{length}.csv") |
| |
| error_1 = round (100 * abs((abs(measured.iloc[:, 1]) - abs(simulated.iloc[:, 1]))/abs(measured.iloc[:, 1])),6) |
| |
| df_error = pd.DataFrame(data=[measured.iloc[:, 0],error_1]).transpose() |
| df_error.to_csv(f"{dirpath}/error_{Id_sim}/{i}_{device}_error_A{width}_P{length}.csv",index= False) |
| |
| # Mean error |
| mean_error = (df_error[f"{d_in}_{corner}"].mean())/6 |
| # Max error |
| max_error = df_error[f"{d_in}_{corner}"].max() |
| # Max error location |
| max_index = max((df_error == max_error).idxmax()) |
| max_location_vgs = (df_error == max_error).idxmax(axis=1)[max_index] |
| max_location_vds = df_error[f"{vn}"][max_index] |
| |
| df_final_ = {'Run no.': f'{i}', 'Temp': f'{temp}', 'Device name': f'{dirpath}', 'Area': f'{width}', 'Perimeter': f'{length}', 'Simulated_Val':f'{Id_sim}','Mean error%':f'{"{:.2f}".format(mean_error)}', 'Max error%':f'{"{:.2f}".format(max_error)} @ {max_location_vgs} & vn (V) = {max_location_vds}'} |
| df_final = df_final.append(df_final_, ignore_index = True) |
| # Max mean error |
| print (df_final) |
| df_final.to_csv (f"{dirpath}/Final_report_{Id_sim}.csv", index = False) |
| out_report = pd.read_csv (f"{dirpath}/Final_report_{Id_sim}.csv") |
| print ("\n",f"Max. mean error = {out_report['Mean error%'].max()}%") |
| print ("=====================================================================================================================================================") |
| |
| def main(): |
| |
| devices = ["dnwps","dnwpw","np_3p3","np_6p0", "nwp_3p3","nwp_6p0","pn_3p3","pn_6p0","sc_diode"] |
| corners = ["typical","ff","ss"] |
| measures = [["iv","Vn1 (V)", " |In1(A)| diode", 103], |
| ["cv","Vj", "diode", 17]] |
| |
| for device in devices: |
| for measure in measures: |
| for corner in corners: |
| # Folder structure of measured values |
| Id_sim, diode_vn, diode_in, no_of_vn_sweeps = measure[0], measure[1], measure[2], measure[3] |
| dirpath = f"{device}_{Id_sim}_{corner}" |
| if os.path.exists(dirpath) and os.path.isdir(dirpath): |
| shutil.rmtree(dirpath) |
| os.makedirs(f"{dirpath}/measured_{Id_sim}",exist_ok=False) |
| |
| # From xlsx to csv |
| read_file = pd.read_excel (f"./0_measured_data/{device}_{Id_sim}.nl_out.xlsx") |
| read_file.to_csv (f"{dirpath}/{device}.csv", index = False, header=True) |
| |
| # Folder structure of simulated values |
| os.makedirs(f"{dirpath}/simulated_{Id_sim}",exist_ok=False) |
| os.makedirs(f"{dirpath}/error_{Id_sim}",exist_ok=False) |
| |
| ext_measured (device,diode_vn,diode_in, Id_sim, corner) |
| |
| ext_simulated(device,diode_vn,diode_in,no_of_vn_sweeps,Id_sim, corner) |
| error_cal (device,diode_vn,diode_in,Id_sim, corner) |
| |
| |
| |
| # # ================================================================ |
| # -------------------------- MAIN -------------------------------- |
| # ================================================================ |
| |
| if __name__ == "__main__": |
| |
| # Args |
| arguments = docopt(__doc__, version='comparator: 0.1') |
| workers_count = os.cpu_count()*2 if arguments["--num_cores"] == None else int(arguments["--num_cores"]) |
| |
| # Calling main function |
| main() |
