The Quantum Efficiency (QE) of the USB camera module is a key parameter for measuring the ability of its sensor to convert incident photons into electrons, directly affecting the imaging quality, especially in low-light or specific wavelength scenarios. The following is an interpretation from the perspectives of definition, influencing factors, testing methods and practical applications:
The definition of quantum efficiency
Quantum efficiency refers to the number of electrons that a sensor can generate with each incident photon at a specific wavelength, usually expressed as a percentage. For example, QE=50% means that 50 out of every 100 photons are converted into electrons.
Formula: QE=
Number of incident photons
The number of electrons produced
X 100%
Physical meaning: The higher the QE, the higher the sensor’s utilization rate of light, and the signal-to-noise ratio (SNR) and dynamic range (DR) are usually better.
2. Core factors influencing quantum efficiency
(1) Sensor type
CMOS sensor
Modern back-illuminated (BSI) CMOS significantly enhances QE, especially in the near-infrared (NIR) band, by optimizing the optical path design (such as moving the photodiode behind the metal wiring layer).
For example, the QE of SONY’s IMX series BSI CMOS can reach more than 80% at a wavelength of 550nm.
CCD sensor
The QE of traditional front-illuminated CCDS is usually lower than that of BSI CMOS. However, some scientific-grade CCDS, through deep depletion layer design, have extremely high QE at specific wavelengths (such as X-rays).
(2) Wavelength dependence
QE varies significantly with wavelength, usually being relatively high in the visible light (400-700nm) range and relatively low in the ultraviolet (UV) and infrared (IR) bands.
For a certain CMOS sensor, QE=70% at 550nm (green light), but it may drop to 30% at 850nm (near-infrared).
(3) Microlens Array
The microlens focuses the incident light onto the photodiode, reducing light loss and increasing QE by 10% ato 20%.
Poor-quality microlenses or improper design may lead to a decrease in edge pixel QE.
(4) Anti-reflective Coating (AR Coating
Coating the sensor surface with an anti-reflection layer can reduce light reflection and enhance QE. For instance, multi-layer AR coatings can increase QE by 5% ato 15% within the visible light range.
3. Testing methods for quantum efficiency
Standard test conditions
Light source: Monochromatic light (such as laser or LED), with a wavelength covering the sensor’s operating range (such as 400-1000nm).
Light intensity: Low light intensity (to avoid saturation), usually controlled by an integrating sphere or monochromator.
Calibration: Use a standard detector with known QE (such as a silicon photodiode) for comparison.
(2) Test procedures
Measure the number of incident photons (calculated by a power meter and wavelength).
Measure the output signal (number of electrons) of the sensor.
Calculate QE = (number of electrons/number of photons) × 100%.
4. The impact of quantum efficiency on the performance of USB cameras
Low-light imaging
High QE sensors can generate more electrons under the same light and have a higher signal-to-noise ratio, making them suitable for scenarios such as night monitoring and astrophotography.
The sensor with QE=80% has less noise in low light than the sensor with QE=50%.
(2) Color reproduction
The wavelength dependence of QE affects the color accuracy. If the sensor has a low QE for red light, the image may appear bluish.
Solution: Compensation through color correction matrix (CCM) or post-processing in RAW format.
(3) Dynamic range
High QE sensors are more prone to saturation in strong light, but modern CMOS can expand the dynamic range through multi-slope ADC or HDR modes.
5. Selection suggestions in practical applications
Industrial inspection: Prioritize the selection of sensors with a high QE at the target wavelength (such as laser wavelength).
Biological microscope: High QE (>70%) is required in the 400-650nm band to reduce exposure time.
Consumer-grade cameras: balancing QE and cost, with QE typically ranging from 50% ato 70%.
6. Clarification of Common Misunderstandings
Misconception 1: High QE = Good picture quality.
The truth is that image quality is also affected by factors such as pixel size, readout noise, and dark current.
Misconception 2: All pixels have the same QE.
The truth is that due to the tilt of the microlens or the loss of the optical path, the QE of edge pixels may be 5% ato 10% lower.
Summary
The quantum efficiency of the USB camera module is the core indicator for evaluating its optical sensitivity, and it needs to be comprehensively analyzed in combination with the sensor type, wavelength requirements and test conditions. High QE sensors have significant advantages in low-light and scientific imaging fields, but cost and performance need to be balanced. When making a choice, it is recommended to refer to the QE curve graph provided by the manufacturer and pay attention to the wavelength coverage range and test standards (such as EMVA 1288).