BSI 2x2 기본 시뮬레이션
표준 2x2 베이어(Bayer) BSI 픽셀 단위 셀(unit cell)을 시뮬레이션하고 양자 효율(QE, Quantum Efficiency) 스펙트럼을 계산하는 완전한 레시피입니다.
Interactive BSI Pixel Stack Cross-Section
Click on any layer to view its material properties and role in the pixel stack.
Click a layer in the stack diagram to see its properties.
Interactive Bayer Pattern Viewer
Explore different color filter array (CFA) patterns. Click a pixel to see its details.
Pattern
RGGB
Unit Cell Size
2x2
Green Ratio
50%
Description
Most common Bayer pattern. Two green pixels per unit cell provide higher luminance resolution, mimicking human vision sensitivity.
학습 내용
- BSI 픽셀 구성을 처음부터 설정하는 방법
- RCWA를 사용한 파장 스윕 실행
- 채널별 QE 스펙트럼 시각화
- 에너지 수지 검증
사전 요구사항
bash
pip install -e ".[rcwa]"Interactive Pixel Stack Builder
Adjust the thickness of each layer in a BSI pixel cross-section. The visualization shows the vertical stack with refractive indices and a scale bar.
Total stack height:4.58 um
구성 설정
설정 딕셔너리를 생성합니다 (또는 YAML에서 로드):
python
config = {
"pixel": {
"pitch": 1.0,
"unit_cell": [2, 2],
"bayer_map": [["R", "G"], ["G", "B"]],
"layers": {
"air": {"thickness": 1.0, "material": "air"},
"microlens": {
"enabled": True,
"height": 0.6,
"radius_x": 0.48,
"radius_y": 0.48,
"material": "polymer_n1p56",
"profile": {"type": "superellipse", "n": 2.5, "alpha": 1.0},
"shift": {"mode": "none"},
},
"planarization": {"thickness": 0.3, "material": "sio2"},
"color_filter": {
"thickness": 0.6,
"pattern": "bayer_rggb",
"materials": {"R": "cf_red", "G": "cf_green", "B": "cf_blue"},
"grid": {"enabled": True, "width": 0.05, "material": "tungsten"},
},
"barl": {
"layers": [
{"thickness": 0.010, "material": "sio2"},
{"thickness": 0.025, "material": "hfo2"},
{"thickness": 0.015, "material": "sio2"},
{"thickness": 0.030, "material": "si3n4"},
]
},
"silicon": {
"thickness": 3.0,
"material": "silicon",
"photodiode": {"position": [0, 0, 0.5], "size": [0.7, 0.7, 2.0]},
"dti": {"enabled": True, "width": 0.1, "material": "sio2"},
},
},
},
"solver": {
"name": "torcwa",
"type": "rcwa",
"params": {"fourier_order": [9, 9], "dtype": "complex64"},
"stability": {
"precision_strategy": "mixed",
"allow_tf32": False,
"fourier_factorization": "li_inverse",
},
},
"source": {
"type": "planewave",
"wavelength": {
"mode": "sweep",
"sweep": {"start": 0.40, "stop": 0.70, "step": 0.01},
},
"angle": {"theta_deg": 0.0, "phi_deg": 0.0},
"polarization": "unpolarized",
},
"compute": {"backend": "auto"},
}시뮬레이션 실행
python
from compass.runners.single_run import SingleRunner
result = SingleRunner.run(config)
print(f"Wavelengths: {result.wavelengths.shape[0]} points "
f"({result.wavelengths[0]*1000:.0f}-{result.wavelengths[-1]*1000:.0f} nm)")
print(f"Pixels: {list(result.qe_per_pixel.keys())}")QE 스펙트럼 시각화
python
from compass.visualization.qe_plot import plot_qe_spectrum
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(8, 5))
plot_qe_spectrum(result, ax=ax)
ax.set_title("BSI 2x2 Bayer (1 um pitch) - QE Spectrum")
plt.tight_layout()
plt.savefig("bsi_2x2_qe.png", dpi=150)
plt.show()에너지 수지 확인
python
from compass.analysis.energy_balance import EnergyBalance
check = EnergyBalance.check(result, tolerance=0.02)
print(f"Valid: {check['valid']}, max error: {check['max_error']:.4f}")Energy Balance: R + T + A = 1
Adjust silicon thickness and BARL quality to see how reflection, transmission, and absorption partition the incident light across the visible spectrum.
Max error:0.00e+0
Mean error:0.00e+0
Validation:✓ Pass
Peak absorption:380 nm
채널별 QE 추출
python
from compass.analysis.qe_calculator import QECalculator
channel_qe = QECalculator.spectral_response(result.qe_per_pixel, result.wavelengths)
for color, (wl, qe) in channel_qe.items():
peak_idx = qe.argmax()
print(f"{color}: peak QE = {qe[peak_idx]:.1%} at {wl[peak_idx]*1000:.0f} nm")예상 출력 (근사값):
B: peak QE = 62.3% at 460 nm
G: peak QE = 71.5% at 540 nm
R: peak QE = 59.8% at 620 nmInteractive QE Spectrum Chart
Explore how silicon thickness, BARL quality, and metal grid width affect the quantum efficiency spectrum of Red, Green, and Blue channels.
Blue peak QE:46.7%
Green peak QE:54.5%
Red peak QE:42.8%
Average QE:48.0%
변형 실험
더 작은 피치 (0.8 um)
python
config["pixel"]["pitch"] = 0.8
config["pixel"]["layers"]["microlens"]["radius_x"] = 0.38
config["pixel"]["layers"]["microlens"]["radius_y"] = 0.38
config["pixel"]["layers"]["silicon"]["photodiode"]["size"] = [0.55, 0.55, 1.6]더 두꺼운 실리콘 (4 um)
python
config["pixel"]["layers"]["silicon"]["thickness"] = 4.0
config["pixel"]["layers"]["silicon"]["dti"]["depth"] = 4.0
config["pixel"]["layers"]["silicon"]["photodiode"]["size"] = [0.7, 0.7, 3.0]마이크로렌즈 없음
python
config["pixel"]["layers"]["microlens"]["enabled"] = False마이크로렌즈 유무에 따른 QE를 비교하여 마이크로렌즈의 기여도를 정량화할 수 있습니다.