Cone cells are natural photoreceptor cells in the retinas of vertebrate eyes. They respond differently to light of different wavelengths and thus responsible for color vision, a sensation in our brain in response to the incidence of mixed wavelength values (i.e. non-monochromatic light) incident on the cone cells. Similarly, this nanotechnology/AI-enabled system will be able to accurately identify colors from different sources.

Technology Overview

In this invention, Northeastern University researchers presented a cyber-physical system for accurately estimating the wavelength of any monochromatic light in the range 325−1100 nm by applying Bayesian inference on the optical transmittance data from a few low-cost, easy-to-fabricate thin film “filters” of layered transition metal dichalcogenides (TMDs) such as MoS2 and WS2. Wavelengths of tested monochromatic light could be estimated with only 1% estimation error over 99% of the stated spectral range, with lowest error values reaching as low as a few ten parts per million (ppm) in a system with only 11 filters. By stepwise elimination of filters with the least contribution toward accuracy, mean estimation accuracy of ∼99% could be obtained even in a two-filter system. Furthermore, this invention provides a statistical approach for selecting the best “filter” material for any intended spectral range based on the spectral variation of transmittance within the desired range of wavelengths. Researchers demonstrate that calibrating the data-driven models for the filters from time to time overcomes the minor drifts in their transmittance values, which allows for use of the same filters indefinitely. This work not only enables the development of simple cyber-physical photodetectors with high accuracy color estimation, but also provides a framework for developing similar cyber-physical systems with drastically reduced complexity.


  • Can detect the color combinations of a light source
  • It is more suitable for the applications where an “eye” is required to perceive the color, which is neither spectroscopy nor image recognition
  • Can be miniaturized into a few micrometers, thereby making it a prime candidate for applications that have size limitations
  • Low-cost which removes the need for expensive characterization instruments


  • Manufacturers of automation hardware for self-driving vehicles (Google), industrial process control (Siemens automation)
  • Manufacturers of robotic vision hardware
  • Manufacturers of Food and Drug industry analysis tools
  • Manufacturers of automation in construction companies 
  • Manufacturers or analytics tools beyond optics, such as X-ray, XPS, Radionuclide identification


  • License
  • Partnering
  • Research collaboration
Patent Information:
-Sensors tech
For Information, Contact:
Mark Saulich
Associate Director of Commercialization
Northeastern University
Davoud Hejazi
Swastik Kar
Sarah Ostadabbas
2D materials
Artificial eye
Artificial intelligence
color sensing
Machine Learning
spectral estimation
Wavelength estimation