Dispersion-free highly accurate color recognition using excitonic 2D materials and machine learning. (October 2022)

• Record Type: Journal Article
• Authors: Hejazi, Davoud; Kari Rezapour, Neda; Ferrier, John; Ostadabbas, Sarah; Kar, Swastik
• Is Part Of: Materials today. Volume 59(2022)
• Page Start: 18
• Page End: 24
• Publication Date: 2022-10
• Subjects: Color recognition -- Dispersion -- 2D materials -- Machine learning -- Machine vision; Materials science -- Periodicals; Metallurgy -- Periodicals; Metal-work -- Periodicals; Biomedical and Dental Materials -- Periodicals; Manufactured Materials -- Periodicals; Metals -- Periodicals; 620.11
• DOI: 10.1016/j.mattod.2022.08.016
• Languages: English
• ISSNs: 1369-7021

Abstract

Graphical abstract: Abstract: Dispersion is accepted as a fundamental step required for analyzing broadband light. The recognition of color by the human eye, its digital reproduction by a camera, or detailed analysis by a spectrometer all utilize dispersion; it is also an inherent component of color detection and machine vision. Here, we present a device (called artificial eye or, A-Eye) that accurately recognizes and reproduces tested colors, without any spectral dispersion . Instead, A-Eye uses N = 3–12 transmissive windows each with unique spectral features resulting from the broadband transmittance and excitonic peak-features of 2D transition metal dichalcogenides. Colored light passing through (and modified by) these windows and incident on a single photodetector generated different photocurrents, and these were used to create a reference database (training set) for 1337 "seen" and 0.55 million synthesized "unseen" colors. By "looking" at test colors modified by these windows, A-Eye can accurately recognize and reproduce "seen" colors with zero deviation from their original spectra and "unseen" colors with only ∼1 % median deviation, using the k -NN algorithm. A-Eye can continuously improve color estimation by adding any corrected guesses to its training database. A-Eye's accurate color recognition dispels the notion that dispersion of colors is a prerequisite for color identification and paves the way for ultra-reliable color-recognition by machines with reduced engineering complexity.