Continuous time-varying biasing approach for spectrally tunable infrared detectors

Area of Science:

• Optoelectronics
• Infrared Spectroscopy
• Materials Science

Background:

• Algorithmic spectral tuning reconstructs object emissivity in the long-wave infrared (LWIR) using discrete photocurrents from quantum dots-in-a-well (DWELL) photodetectors.
• The prior method required a post-processing step to combine discrete photocurrents obtained at static biases.

Purpose of the Study:

• To generalize the algorithmic spectral-tuning technique by employing continuously varying biasing voltage.
• To eliminate the need for post-processing steps in spectral reconstruction.
• To enable real-time spectral tuning in focal-plane arrays.

Main Methods:

• Developed an algorithm for designing time-varying bias for arbitrary spectral-sensing windows.
• Replaced serial static biases with a continuously varying voltage over an extended acquisition time.
• Validated the technique using simulations for spectrometry and object classification with experimental DWELL data.

Main Results:

• Eliminated the post-processing superposition of discrete photocurrents.
• Demonstrated a generalized algorithmic spectral-tuning technique applicable to focal-plane arrays.
• Validated the continuous-time biasing approach under realistic signal-to-noise ratios.

Conclusions:

• The generalized technique enables algorithmic spectral tuning within focal-plane arrays in real-time.
• Continuous-time biasing simplifies spectral reconstruction and reduces computational load.
• This advancement facilitates on-chip spectral analysis without on-sensor multiplications and additions.