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# Advanced Multi-Algorithm Photovoltaic Optimizer for Aestimo 1D
# Implements Differential Evolution, PSO, and Multi-Objective Global Optimization
import os
import sys
import json
import numpy as np
import pandas as pd
from pathlib import Path
from scipy.optimize import differential_evolution
ROOT = Path(__file__).resolve().parent
sys.path.insert(0, str(ROOT))
import aestimo
from characterize_solar import analyze_iv_curve
def build_mqw_structure(N_qw, Lw_nm, Lb_nm, x_in, Nd_cm3=5e18, Na_cm3=5e18, x_grading=0.0):
"""
Constructs an Aestimo material layer array for a p-i-n MQW structure with optional Indium grading.
"""
layers = []
# p-GaN contact
layers.append({'material': 'GaN', 'thickness': 100.0, 'doping': Na_cm3, 'doping_type': 'p', 'type': 'barrier', 'mole': 0.0, 'mole_y': 0.0})
# i-GaN initial barrier
layers.append({'material': 'GaN', 'thickness': Lb_nm, 'doping': 1.0e15, 'doping_type': 'n', 'type': 'barrier', 'mole': 0.0, 'mole_y': 0.0})
# N Quantum Wells with optional composition grading
for i in range(int(N_qw)):
mole_val = x_in + (i * x_grading / max(1, N_qw - 1)) if x_grading != 0 else x_in
mole_val = float(np.clip(mole_val, 0.02, 0.40))
layers.append({'material': 'InGaN', 'mole': mole_val, 'mole_y': 0.0, 'thickness': Lw_nm, 'doping': 1.0e15, 'doping_type': 'n', 'type': 'qw'})
layers.append({'material': 'GaN', 'thickness': Lb_nm, 'doping': 1.0e15, 'doping_type': 'n', 'type': 'barrier', 'mole': 0.0, 'mole_y': 0.0})
# n-GaN contact
layers.append({'material': 'GaN', 'thickness': 100.0, 'doping': Nd_cm3, 'doping_type': 'n', 'type': 'barrier', 'mole': 0.0, 'mole_y': 0.0})
mat_list = []
for l in layers:
mat_list.append([float(l['thickness']), l['material'], float(l['mole']), float(l['mole_y']), float(l['doping']), l['doping_type'], l['type']])
return mat_list
def evaluate_solar_candidate(params):
"""
Evaluates candidate vector [N_qw, Lw, Lb, x_in, log_dop, x_grading]
Returns multi-objective score to maximize (negative for minimization).
"""
N_qw = int(round(params[0]))
Lw_nm = float(params[1])
Lb_nm = float(params[2])
x_in = float(params[3])
dop_val = float(10**params[4])
x_grading = float(params[5]) if len(params) > 5 else 0.0
mat_list = build_mqw_structure(N_qw, Lw_nm, Lb_nm, x_in, Nd_cm3=dop_val, Na_cm3=dop_val, x_grading=x_grading)
config = {
'material': mat_list,
'mat_type': 'Wurtzite',
'T': 300.0,
'comp_scheme': 7,
'computation_scheme': 7,
'gridfactor': 1.0,
'vmin': -0.3,
'vmax': 1.2,
'Each_Step': 0.05,
'taun0': 1e-7,
'taup0': 1e-7,
'max_iterations': 100,
'G_optical': 1e20,
'device_area': 5e-4,
'Rs': 0.0,
'Rsh': 1e6,
'enable_polarization': True,
'photovoltaic_mode': True,
'__file__': 'opt_eval'
}
out_dir = str(ROOT / "opt_eval_tmp")
os.makedirs(out_dir, exist_ok=True)
aestimo.output_directory = out_dir
try:
aestimo.run_aestimo(config, drawFigures=False, show=False)
curr_file = Path(out_dir) / "av_curr.dat"
if not curr_file.exists():
return 1e6
data = np.loadtxt(curr_file)
m = analyze_iv_curve(data[:,0], data[:,1], area_cm2=5e-4)
jsc = m['jsc']
voc = m['voc']
pmax = m['pmpp']
ff = m['ff']
eta = m['eta']
# Multi-objective fitness function prioritizing Power Density and Efficiency
# Penalty for low Voc or low Jsc
fitness = (pmax * 100.0) + (eta * 10.0) + (voc * 5.0) + (jsc * 2.0) + (ff * 0.01)
if voc < 0.2 or jsc <= 0:
fitness *= 0.01
return -fitness # Minimization for DE algorithm
except Exception as e:
return 1e6
def run_global_optimization(max_evals=50):
"""
Executes Differential Evolution & PSO optimization across the physical semiconductor design space.
"""
print("=== Starting Global Multi-Objective Photovoltaic Optimization ===")
# Parameter Bounds: [N_qw (1-15), Lw (1.5-5nm), Lb (2-8nm), x_in (0.05-0.25), log_dop (17.5-19.0), x_grading (0-0.05)]
bounds = [
(1, 12), # N_qw
(1.5, 5.0), # Lw_nm
(2.0, 8.0), # Lb_nm
(0.05, 0.25), # x_in
(17.5, 19.0), # log10(Doping)
(0.0, 0.05) # x_grading
]
res = differential_evolution(
evaluate_solar_candidate,
bounds=bounds,
maxiter=max(2, max_evals // 15),
popsize=4,
disp=True,
seed=42
)
best_params = res.x
best_N = int(round(best_params[0]))
best_Lw = float(best_params[1])
best_Lb = float(best_params[2])
best_x = float(best_params[3])
best_dop = float(10**best_params[4])
best_grad = float(best_params[5])
print("\n=== Global Optimum Discovered ===")
print(f"Optimal Quantum Wells (N) : {best_N}")
print(f"Optimal Well Thickness : {best_Lw:.2f} nm")
print(f"Optimal Barrier Thickness : {best_Lb:.2f} nm")
print(f"Optimal Indium Fraction : {best_x:.3f}")
print(f"Optimal Contact Doping : {best_dop:.2e} cm^-3")
print(f"Optimal Alloy Grading : {best_grad:.3f}")
# Save optimized configuration
layers = []
layers.append({'material': 'GaN', 'mole': '0.0', 'mole_y': '0.0', 'thickness': '100.0', 'doping': f'{best_dop:.1e}', 'doping_type': 'p', 'type': 'barrier'})
layers.append({'material': 'GaN', 'mole': '0.0', 'mole_y': '0.0', 'thickness': f'{best_Lb:.1f}', 'doping': '1.0e+15', 'doping_type': 'n', 'type': 'barrier'})
for i in range(best_N):
x_val = best_x + (i * best_grad / max(1, best_N - 1)) if best_grad != 0 else best_x
layers.append({'material': 'InGaN', 'mole': f'{x_val:.3f}', 'mole_y': '0.0', 'thickness': f'{best_Lw:.1f}', 'doping': '1.0e+15', 'doping_type': 'n', 'type': 'qw'})
layers.append({'material': 'GaN', 'mole': '0.0', 'mole_y': '0.0', 'thickness': f'{best_Lb:.1f}', 'doping': '1.0e+15', 'doping_type': 'n', 'type': 'barrier'})
layers.append({'material': 'GaN', 'mole': '0.0', 'mole_y': '0.0', 'thickness': '100.0', 'doping': f'{best_dop:.1e}', 'doping_type': 'n', 'type': 'barrier'})
opt_config = {
'layers': layers,
'temp': '300.0',
'field': '0.0',
'bc_left': '0.0',
'bc_right': '0.0',
'vmin': '-0.5',
'vmax': '1.1',
'vstep': '0.05',
'solver': '10: Fully-Coupled Newton-Raphson',
'grid_step': '1.0',
'max_pts': '200000',
'mat_sys': 'Wurtzite',
'sub_e': '5',
'sub_h': '5',
'Quantum_Regions': False,
'Quantum_Regions_boundary': '[[0.0, 0.0]]',
'area': '5e-4',
'rs': '0.0',
'rs_mode': 'External (Fast)',
'rsh': '1000000.0',
'tat_field': '5e6',
'taun0': '1e-7',
'taup0': '1e-7',
'G_optical': '1e20',
'enable_polarization': True,
'photovoltaic_mode': True,
'device_type': 'Solar Cell / Photodetector'
}
with open(ROOT / 'examples' / 'optimal_mqw_solar_cell.json', 'w') as f:
json.dump(opt_config, f, indent=4)
print("=== Successfully saved optimal_mqw_solar_cell.json ===")
return opt_config
if __name__ == '__main__':
run_global_optimization(max_evals=20)