| """ |
| 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,sweep_x,sweep_y,sim_val): |
| |
| # Get dimensions used for each device |
| dimensions = pd.read_csv(f"{device}/{device}.csv",usecols=["W (um)" , "L (um)"]) |
| loops = int(dimensions["L (um)"].count()/2) |
| |
| # Extracting measured values for each W & L |
| for i in range (0,loops): |
| width = dimensions["W (um)"].iloc[int(i)] |
| length = dimensions["L (um)"].iloc[int(i)] |
| # Special case for 1st measured values |
| if i == 0 : |
| # measured Cgc |
| if sim_val == "Cgc": |
| col_list = [sweep_x,sweep_y[0],sweep_y[1],sweep_y[2],sweep_y[3],sweep_y[4]] |
| df_measured = pd.read_csv(f"{device}/{device}.csv",usecols=col_list) |
| df_measured.columns = [sweep_x,sweep_y[0],sweep_y[1],sweep_y[2],sweep_y[3],sweep_y[4]] |
| else: |
| # measured Cgs & Cgd |
| if sim_val == "Cgs": |
| col_list = [sweep_x,sweep_y[0],sweep_y[1],sweep_y[2],sweep_y[3]] |
| else: |
| col_list = [sweep_x,f"{sweep_y[0]}.1",f"{sweep_y[1]}.1",f"{sweep_y[2]}.1",f"{sweep_y[3]}.1"] |
| df_measured = pd.read_csv(f"{device}/{device}.csv",usecols=col_list) |
| df_measured.columns = [sweep_x,sweep_y[0],sweep_y[1],sweep_y[2],sweep_y[3]] |
| df_measured.to_csv(f"{device}/measured_{sim_val}/{i}_measured_W{width}_L{length}.csv", index = False) |
| else: |
| # measured Cgc |
| if sim_val == "Cgc": |
| col_list = [sweep_x,f"{sweep_y[0]}.{i}",f"{sweep_y[1]}.{i}",f"{sweep_y[2]}.{i}",f"{sweep_y[3]}.{i}",f"{sweep_y[4]}.{i}"] |
| df_measured = pd.read_csv(f"{device}/{device}.csv",usecols=col_list) |
| df_measured.columns = [sweep_x,f"{sweep_y[0]}",f"{sweep_y[1]}",f"{sweep_y[2]}",f"{sweep_y[3]}",f"{sweep_y[4]}"] |
| else: |
| # measured Cgs & Cgd |
| cgs_index = 2*i |
| cgd_index = cgs_index + 1 |
| if sim_val == "Cgs": |
| col_list = [sweep_x,f"{sweep_y[0]}.{cgs_index}",f"{sweep_y[1]}.{cgs_index}",f"{sweep_y[2]}.{cgs_index}",f"{sweep_y[3]}.{cgs_index}"] |
| else: |
| col_list = [sweep_x,f"{sweep_y[0]}.{cgd_index}",f"{sweep_y[1]}.{cgd_index}",f"{sweep_y[2]}.{cgd_index}",f"{sweep_y[3]}.{cgd_index}"] |
| df_measured = pd.read_csv(f"{device}/{device}.csv",usecols=col_list) |
| df_measured.columns = [sweep_x,f"{sweep_y[0]}",f"{sweep_y[1]}",f"{sweep_y[2]}",f"{sweep_y[3]}"] |
| |
| df_measured.to_csv(f"{device}/measured_{sim_val}/{i}_measured_W{width}_L{length}.csv", index = False) |
| |
| def ext_simulated(device,sweep_x,sweep_y,vds_sweep,sim_val): |
| |
| # Get dimensions used for each device |
| dimensions = pd.read_csv(f"{device}/{device}.csv",usecols=["W (um)" , "L (um)"]) |
| loops = int(dimensions["L (um)"].count()/2) |
| |
| netlist_tmp = f"./device_netlists_{sim_val}/{device}.spice" |
| for i in range (0,loops): |
| width = dimensions["W (um)"].iloc[int(i)] |
| length = dimensions["L (um)"].iloc[int(i)] |
| if i == 0: |
| nf = 20 |
| else: |
| nf = 1 |
| with open(netlist_tmp) as f: |
| tmpl = Template(f.read()) |
| os.makedirs(f"{device}/{device}_netlists_{sim_val}",exist_ok=True) |
| with open(f"{device}/{device}_netlists_{sim_val}/{i}_{device}_netlist_W{width}_L{length}.spice", "w") as netlist: |
| netlist.write(tmpl.render(width = width,length = length,i = i, nf = nf )) |
| netlist_path = f"{device}/{device}_netlists_{sim_val}/{i}_{device}_netlist_W{width}_L{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"{device}/simulated_{sim_val}/{i}_simulated_W{width}_L{length}.csv",header=None, delimiter=r"\s+") |
| df_simulated.to_csv(f"{device}/simulated_{sim_val}/{i}_simulated_W{width}_L{length}.csv",index= False) |
| |
| # empty array to append in it shaped (vds_sweep, number of trials + 1) |
| new_array = np.empty((vds_sweep, 1+int(df_simulated.shape[0]/vds_sweep))) |
| new_array[:, 0] = df_simulated.iloc[:vds_sweep, 0] |
| times = int(df_simulated.shape[0]/vds_sweep) |
| |
| for j in range(times): |
| new_array[:, (j+1)] = df_simulated.iloc[j*vds_sweep:(j+1)*vds_sweep, 1] |
| |
| # Writing final simulated data |
| df_simulated = pd.DataFrame(new_array) |
| df_simulated.to_csv(f"{device}/simulated_{sim_val}/{i}_simulated_W{width}_L{length}.csv",index= False) |
| if sim_val == "Cgc": |
| df_simulated.columns = [sweep_x,sweep_y[0],sweep_y[1],sweep_y[2],sweep_y[3],sweep_y[4]] |
| else: |
| df_simulated.columns = [sweep_x,sweep_y[0],sweep_y[1],sweep_y[2],sweep_y[3]] |
| df_simulated.to_csv(f"{device}/simulated_{sim_val}/{i}_simulated_W{width}_L{length}.csv",index= False) |
| |
| def error_cal(device,sweep_x,sweep_y,sim_val): |
| |
| # Get dimensions used for each device |
| dimensions = pd.read_csv(f"{device}/{device}.csv",usecols=["W (um)" , "L (um)"]) |
| loops = int(dimensions["L (um)"].count()/2) |
| df_final = pd.DataFrame() |
| for i in range (0,loops): |
| width = dimensions["W (um)"].iloc[int(i)] |
| length = dimensions["L (um)"].iloc[int(i)] |
| |
| measured = pd.read_csv(f"{device}/measured_{sim_val}/{i}_measured_W{width}_L{length}.csv") |
| simulated = pd.read_csv(f"{device}/simulated_{sim_val}/{i}_simulated_W{width}_L{length}.csv") |
| |
| error_1 = round (100 * (abs(measured.iloc[:, 1]) - abs(simulated.iloc[:, 1]))/abs(measured.iloc[:, 1]),6) |
| error_2 = round (100 * (abs(measured.iloc[:, 2]) - abs(simulated.iloc[:, 2]))/abs(measured.iloc[:, 2]),6) |
| error_3 = round (100 * (abs(measured.iloc[:, 3]) - abs(simulated.iloc[:, 3]))/abs(measured.iloc[:, 3]),6) |
| error_4 = round (100 * (abs(measured.iloc[:, 4]) - abs(simulated.iloc[:, 4]))/abs(measured.iloc[:, 4]),6) |
| if sim_val == "Cgc": |
| error_5 = round (100 * (abs(measured.iloc[:, 5]) - abs(simulated.iloc[:, 5]))/abs(measured.iloc[:, 5]),6) |
| df_error = pd.DataFrame(data=[measured.iloc[:, 0],error_1,error_2,error_3,error_4,error_5]).transpose() |
| else: |
| df_error = pd.DataFrame(data=[measured.iloc[:, 0],error_1,error_2,error_3,error_4]).transpose() |
| df_error.to_csv(f"{device}/error_{sim_val}/{i}_{device}_error_W{width}_L{length}.csv",index= False) |
| |
| # Mean error |
| if sim_val == "Cgc": |
| mean_error = (df_error[sweep_y[0]].mean() + df_error[sweep_y[1]].mean() + df_error[sweep_y[2]].mean() + |
| df_error[sweep_y[3]].mean() + df_error[sweep_y[4]].mean())/5 |
| else: |
| mean_error = (df_error[sweep_y[0]].mean() + df_error[sweep_y[1]].mean() + df_error[sweep_y[2]].mean() + |
| df_error[sweep_y[3]].mean())/4 |
| # Max error |
| if sim_val == "Cgc": |
| max_error = df_error[[sweep_y[0],sweep_y[1],sweep_y[2],sweep_y[3],sweep_y[4]]].max().max() |
| else: |
| max_error = df_error[[sweep_y[0],sweep_y[1],sweep_y[2],sweep_y[3]]].max().max() |
| # Max error location |
| max_index = max((df_error == max_error).idxmax()) |
| max_location_sweep_y = (df_error == max_error).idxmax(axis=1)[max_index] |
| max_location_sweep_x = df_error[sweep_x][max_index] |
| |
| df_final_ = {'Run no.': f'{i}', 'Temp': f'25', 'Device name': f'{device}', 'Width': f'{width}', 'Length': f'{length}', 'Simulated_Val':f'{sim_val}','Mean error%':f'{"{:.2f}".format(mean_error)}', 'Max error%':f'{"{:.2f}".format(max_error)} @ {max_location_sweep_y} & {sweep_x}= {max_location_sweep_x}'} |
| df_final = df_final.append(df_final_, ignore_index = True) |
| # Max mean error |
| print (df_final) |
| df_final.to_csv (f"{device}/Final_report_{sim_val}.csv", index = False) |
| out_report = pd.read_csv (f"{device}/Final_report_{sim_val}.csv") |
| print ("\n",f"Max. mean error = {out_report['Mean error%'].max()}%") |
| print ("=====================================================================================================================================================") |
| |
| def main(): |
| |
| devices = ["nmos_3p3_cv" , "pmos_3p3_cv" ] |
| measured_data = ["3p3_cv"] |
| nmos_vds = "Vds (V)" |
| pmos_vds = "-Vds (V)" |
| nmos_vgs = "Vgs (V)" |
| pmos_vgs = "-Vgs (V)" |
| nmos_rds = "Rds" |
| cgc_sim = "Cgc" |
| cgs_sim = "Cgs" |
| cgd_sim = "Cgd" |
| Rds_sim = "Rds" |
| mos_3p3_vbs_sweep = 67 |
| mos_3p3_vgs_sweep = 34 |
| |
| mos_6p0_vgs_sweep = 133 |
| |
| nmos3p3_vgs = ["Vgs=0" , "Vgs=1.1" , "Vgs=2.2" , "Vgs=3.3" ] |
| pmos3p3_vgs = ["Vgs=-0" , "Vgs=-1.1" , "Vgs=-2.2" , "Vgs=-3.3"] |
| # pmos3p3_vgs = [-0.8 , -1.3 , -1.8 , -2.3 , -2.8 , -3.3] |
| # nmos6p0_vgs = [ 1 , 2 , 3 , 4 , 5 , 6] |
| # pmos6p0_vgs = [-1 , -2 , -3 , -4 , -5 , -6] |
| # nmos6p0_nat_vgs = [ 0.25 , 1.4 , 2.55 , 3.7 , 4.85 , 6] |
| |
| nmos3p3_vbs = ["Vbs=0" , "Vbs=-0.825" , "Vbs=-1.65" , "Vbs=-2.475" , "Vbs=-3.3"] |
| pmos3p3_vbs = ["Vbs=-0" , "Vbs=0.825" , "Vbs=1.65" , "Vbs=2.475" , "Vbs=3.3" ] |
| # nmos6p0_vbs = [ 0 , -0.75 , -1.5 , -2.25 , -3] |
| # pmos6p0_vbs = [ 0 , 0.75 , 1.5 , 2.25 , 3] |
| # nmos6p0_nat_vbs = [ 0 , -0.75 , -1.5 , -2.25 , -3] |
| |
| for device in devices: |
| # Folder structure of measured values |
| dirpath = f"{device}" |
| if os.path.exists(dirpath) and os.path.isdir(dirpath): |
| shutil.rmtree(dirpath) |
| os.makedirs(f"{device}/measured_{cgc_sim}",exist_ok=False) |
| os.makedirs(f"{device}/measured_{cgs_sim}",exist_ok=False) |
| os.makedirs(f"{device}/measured_{cgd_sim}",exist_ok=False) |
| |
| # From xlsx to csv |
| read_file = pd.read_excel (f"./measured_data/{measured_data[0]}.nl_out.xlsx") |
| read_file.to_csv (f"{device}/{device}.csv", index = False, header=True) |
| |
| # Folder structure of simulated values |
| os.makedirs(f"{device}/simulated_{cgc_sim}",exist_ok=False) |
| os.makedirs(f"{device}/simulated_{cgs_sim}",exist_ok=False) |
| os.makedirs(f"{device}/simulated_{cgd_sim}",exist_ok=False) |
| os.makedirs(f"{device}/error_{cgc_sim}",exist_ok=False) |
| os.makedirs(f"{device}/error_{cgs_sim}",exist_ok=False) |
| os.makedirs(f"{device}/error_{cgd_sim}",exist_ok=False) |
| |
| |
| # =========== nmos_3p3_cv ============== |
| # Cgc |
| ext_measured ("nmos_3p3_cv",nmos_vgs,nmos3p3_vbs,cgc_sim) |
| ext_simulated("nmos_3p3_cv",nmos_vgs,nmos3p3_vbs,mos_3p3_vbs_sweep,cgc_sim) |
| error_cal ("nmos_3p3_cv",nmos_vgs,nmos3p3_vbs,cgc_sim) |
| |
| # Cgs |
| ext_measured ("nmos_3p3_cv",nmos_vds,nmos3p3_vgs,cgs_sim) |
| ext_simulated("nmos_3p3_cv",nmos_vds,nmos3p3_vgs,mos_3p3_vgs_sweep,cgs_sim) |
| error_cal ("nmos_3p3_cv",nmos_vds,nmos3p3_vgs,cgs_sim) |
| |
| # Cgd |
| ext_measured ("nmos_3p3_cv",nmos_vds,nmos3p3_vgs,cgd_sim) |
| ext_simulated("nmos_3p3_cv",nmos_vds,nmos3p3_vgs,mos_3p3_vgs_sweep,cgd_sim) |
| error_cal ("nmos_3p3_cv",nmos_vds,nmos3p3_vgs,cgd_sim) |
| |
| |
| # =========== nmos_3p3_cv ============== |
| # Cgc |
| ext_measured ("pmos_3p3_cv",pmos_vgs,pmos3p3_vbs,cgc_sim) |
| ext_simulated("pmos_3p3_cv",pmos_vgs,pmos3p3_vbs,mos_3p3_vbs_sweep,cgc_sim) |
| error_cal ("pmos_3p3_cv",pmos_vgs,pmos3p3_vbs,cgc_sim) |
| |
| # Cgs |
| ext_measured ("pmos_3p3_cv",pmos_vgs,pmos3p3_vgs,cgs_sim) |
| ext_simulated("pmos_3p3_cv",pmos_vgs,pmos3p3_vgs,mos_3p3_vgs_sweep,cgs_sim) |
| error_cal ("pmos_3p3_cv",pmos_vgs,pmos3p3_vgs,cgs_sim) |
| |
| # Cgd |
| ext_measured ("pmos_3p3_cv",pmos_vgs,pmos3p3_vgs,cgd_sim) |
| ext_simulated("pmos_3p3_cv",pmos_vgs,pmos3p3_vgs,mos_3p3_vgs_sweep,cgd_sim) |
| error_cal ("pmos_3p3_cv",pmos_vgs,pmos3p3_vgs,cgd_sim) |
| |
| # =========== pmos_3p3_iv ============== |
| |
| |
| # =========== nmos_6p0_iv ============== |
| |
| |
| # =========== pmos_6p0_iv ============== |
| |
| |
| # ============ nmos_3p3_sab_iv ============= # Error in ngspice |
| |
| |
| # ============ nmos_6p0_nat_iv ============= |
| |
| |
| # # ================================================================ |
| # -------------------------- 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() |
