Feature-Enhanced Artificial Visual Perception Based on Superlinear Voltage-Reflectance Responses of Electrochromic Arrays

The mimicking of human visual information processing, recognition, and storage is attracting intense interest in the field of artificial intelligence technologies. Electrochromic arrays, directly displaying images that can act as data sets for neuromorphic computing, are advantageous in providing a pathway to energy-efficient artificial visual perception. However, improvement of the recognition accuracy of low-contrast images is still a tremendous challenge. To establish a feature-enhancement strategy, a superlinear relationship between the responses and intensities of the input signals needs to be established. In this paper, reflective electrochromic arrays are fabricated by electrodeposition of Prussian blue and a bladed coating of carbon paste. The arrays exhibit a superlinear response of reflectance values at different voltage values. The reflectance almost remains stable in the range from 1.2 to −0.7 V and increases sharply below −0.7 V. The maximum reflectance modulation is as high as 74.8%. To enhance features of low-contrast digital images that are hardly recognized artificially, voltage values are generated proportionally from the grayscales of each pixel of the low-contrast images. Next, the electrochromic arrays display feature-enhanced digital images by controlling the voltage at each pixel. Consequently, artificial neural networks and diffractive neural networks take only 32 and 20 epochs to achieve 100% accuracy in low-contrast image recognition, respectively. The artificial visual perception design has great potential to realize sensory systems for pattern recognition from complex environments.