| """ |
| 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"Xyce -hspice-ext all {file_name} -l {file_name}.log") |
| |
| def ext_measured(device,vn,d_in, cv_sim, corner,start): |
| |
| # Get dimensions used for each device |
| dirpath = f"{device}_{cv_sim}_{corner}" |
| |
| # Extracting measured values for each W & L |
| for i in range (start,4+start): |
| if i == 0+start: width = 100 ; length = 100 |
| if i == 1+start: width = 5 ; length = 5 |
| if i == 2+start: width = 100 ; length = 5 |
| if i == 3+start: width = 5 ; length = 100 |
| |
| if i == 0 : |
| # measured cv |
| col_list = [f"Vj",f"mimcap_{corner}"] |
| df_measured = pd.read_csv(f"{dirpath}/{device}.csv",usecols=col_list) |
| df_measured.to_csv(f"{dirpath}/measured_{cv_sim}/{i-start}_measured_w{width}_l{length}.csv", index=False) |
| else: |
| # measured cv |
| col_list = [f"Vj",f"mimcap_{corner}.{i}"] |
| df_measured = pd.read_csv(f"{dirpath}/{device}.csv",usecols=col_list) |
| df_measured.columns = [f"Vj",f"mimcap_{corner}"] |
| df_measured.to_csv(f"{dirpath}/measured_{cv_sim}/{i-start}_measured_w{width}_l{length}.csv", index=False) |
| |
| def ext_simulated(device,vn,d_in,cv_sim, corner,start,voltage): |
| |
| # Get dimensions used for each device |
| dirpath = f"{device}_{cv_sim}_{corner}" |
| netlist_tmp = f"./device_netlists/mimcap.spice" |
| |
| # Extracting measured values for each W & L |
| for i in range (start,4+start): |
| if i == 0+start: width = 100 ; length = 100 |
| if i == 1+start: width = 5 ; length = 5 |
| if i == 2+start: width = 100 ; length = 5 |
| if i == 3+start: width = 5 ; length = 100 |
| |
| with open(netlist_tmp) as f: |
| tmpl = Template(f.read()) |
| os.makedirs(f"{dirpath}/{device}_netlists_{cv_sim}",exist_ok=True) |
| with open(f"{dirpath}/{device}_netlists_{cv_sim}/{i-start}_{device}_netlist_w{width}_l{length}.spice", "w") as netlist: |
| netlist.write(tmpl.render(device = device, width = width, length = length , corner = corner , voltage = voltage)) |
| netlist_path = f"{dirpath}/{device}_netlists_{cv_sim}/{i-start}_{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 = [] |
| # Writing simulated data |
| for j in range(len([x for x in os.listdir(f"{dirpath}/{device}_netlists_{cv_sim}") if f"{i-start}_{device}_netlist_w{width}_l{length}.spice.ma" in x])): |
| with open(f"{dirpath}/{device}_netlists_{cv_sim}/{i-start}_{device}_netlist_w{width}_l{length}.spice.ma{j}") as f: |
| cap = 1000000/(float(next(f).replace("FREQ = ", ""))*2*np.pi) |
| df_simulated.append(cap) |
| |
| # zero array to append in it shaped (vn_sweeps, number of trials + 1) |
| new_array = np.zeros((len(df_simulated), 2)) |
| new_array[:len(df_simulated), 0] = df_simulated |
| new_array[:len(df_simulated), 1] = df_simulated |
| |
| # Writing final simulated data |
| df_simulated = pd.DataFrame(new_array) |
| df_simulated.columns = [f"Vj",f"mimcap_{corner}"] |
| df_simulated.to_csv(f"{dirpath}/simulated_{cv_sim}/{i-start}_simulated_w{width}_l{length}.csv",index= False) |
| |
| def error_cal(device,vn,d_in,Id_sim, corner,start): |
| |
| # Get dimensions used for each device |
| dirpath = f"{device}_{Id_sim}_{corner}" |
| df_final = pd.DataFrame() |
| for i in range (start,4+start): |
| if i == 0+start: width = 100 ; length = 100 |
| if i == 1+start: width = 5 ; length = 5 |
| if i == 2+start: width = 100 ; length = 5 |
| if i == 3+start: width = 5 ; length = 100 |
| |
| measured = pd.read_csv(f"{dirpath}/measured_{Id_sim}/{i-start}_measured_w{width}_l{length}.csv") |
| simulated = pd.read_csv(f"{dirpath}/simulated_{Id_sim}/{i-start}_simulated_w{width}_l{length}.csv") |
| |
| error_1 = round (100 * abs((abs(measured.iloc[:, 1]) - abs(simulated.iloc[:, 1]))/abs(measured.iloc[:, 1])),8) |
| |
| df_error = pd.DataFrame(data=[measured.iloc[:, 0],error_1]).transpose() |
| df_error.to_csv(f"{dirpath}/error_{Id_sim}/{i-start}_{device}_error_w{width}_l{length}.csv",index= False) |
| |
| # Mean error |
| mean_error = (df_error[f"mimcap_{corner}"].mean()) |
| # Max error |
| max_error = df_error[f"mimcap_{corner}"].max() |
| |
| df_final_ = {'Run no.': f'{i-start}', 'Device name': f'{dirpath}', 'Width': f'{width}', 'Length': f'{length}', 'Simulated_Val':f'{Id_sim}','Mean error%':f'{"{:.2f}".format(mean_error)}', 'Max error%':f'{"{:.2f}".format(max_error)} '} |
| 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(): |
| |
| # 3p3 |
| corners = ["ss" , "typical","ff"] |
| devices = ["mim_1p5fF" , "mim_1p0fF" , "mim_2p0fF"] |
| measure = ["cv","corners", "CV (fF)"] |
| voltage = ["-3.0 3.0 0.1"] |
| start = 0 |
| for corner in corners: |
| for device in devices: |
| # Folder structure of measured values |
| cv_sim, cap_vn, cap_in = measure[0], measure[1], measure[2] |
| dirpath = f"{device}_{cv_sim}_{corner}" |
| if os.path.exists(dirpath) and os.path.isdir(dirpath): |
| shutil.rmtree(dirpath) |
| os.makedirs(f"{dirpath}/measured_{cv_sim}",exist_ok=False) |
| |
| # From xlsx to csv |
| read_file = pd.read_excel (f"../../180MCU_SPICE_DATA/Cap/mimcap_fc.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_{cv_sim}",exist_ok=False) |
| os.makedirs(f"{dirpath}/error_{cv_sim}",exist_ok=False) |
| |
| ext_measured (device,cap_vn,cap_in, cv_sim, corner,start) |
| ext_simulated(device,cap_vn,cap_in,cv_sim, corner,start,voltage[0]) |
| error_cal (device,cap_vn,cap_in,cv_sim, corner,start) |
| start = start + 4 |
| start = 0 |
| |
| |
| for corner in corners: |
| for device in devices: |
| # Folder structure of measured values |
| cv_sim, cap_vn, cap_in = measure[0], measure[1], measure[2] |
| error_cal (device,cap_vn,cap_in,cv_sim, corner,start) |
| start = start + 4 |
| start = 0 |
| |
| |
| |
| # # ================================================================ |
| # -------------------------- 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() |
