Bias-Tunable Quantum Well Infrared Photodetector

Area of Science:

• Optoelectronics
• Infrared Sensing
• Quantum Well Devices

Background:

• Advancements in AI object recognition necessitate sophisticated imaging sensors.
• Cognitive tunable imaging sensors are crucial for enhanced AI capabilities.
• Quantum Well Infrared Photodetectors (QWIPs) offer potential for spectral tunability.

Purpose of the Study:

• To demonstrate a tunable infrared sensor with bias-controlled spectral responsivity.
• To explore the application of tunable QWIPs in AI object recognition.
• To enable detection across mid-wave (MWIR) and long-wave (LWIR) infrared regions.

Main Methods:

• Designed and fabricated a Quantum Well Infrared Photodetector (QWIP) with asymmetrically doped double quantum wells.
• Utilized Schrödinger-Poisson solver to determine electronic transition energies for design rules.
• Grew the QWIP structure using molecular beam epitaxy (MBE) with GaAs/AlGaAs layers.
• Characterized sensor performance using blackbody radiation and Fourier-transform infrared spectroscopy (FTIR) down to 77 K.

Main Results:

• Achieved spectral tunability by controlling electron population in quantum wells via applied bias.
• Demonstrated an order of magnitude control over the ratio of responsivities at ~5.5 µm and ~8 µm.
• Successfully tuned spectral responsivity between the mid-wave and long-wave infrared regions.
• Laid the groundwork for AI object recognition using tunable QWIP sensors through image transformation experiments.

Conclusions:

• The developed QWIP sensor exhibits effective spectral tunability between MWIR and LWIR bands.
• The bias-controlled electron population in asymmetrically doped double quantum wells enables spectral adjustment.
• This tunable infrared sensing technology is promising for advanced AI-driven object recognition applications.