KAUST researchers have developed an artificial electronic retina based on perovskite materials, that can "see" in a similar way to the human vision system and can recognize handwritten digits.
The team designed and fabricated an array of photoreceptors that detect the intensity of visible light via a change in electrical capacitance, mimicking the behavior of the eye's rod retina cells. When the array was connected to an electronic CMOS-sensing circuit and a spiking neural network (a single-layer network with 100 output neurons), it was able to recognize handwritten numbers with an accuracy of around 70%.
The photoreceptor array is made by sandwiching a material with suitable optical and dielectric properties between a bottom aluminum electrode and a patterned top electrode of indium tin oxide to form a pixelated array of miniature light-sensitive metal-insulator-metal capacitors. The array is made on a thin substrate of polyimide so that it is flexible and can be curved as desired, including a hemispherical shape mimicking the human eye.
In selecting materials for their photoreceptor, the KAUST team used a hybrid material of perovskite (methylammonium lead bromide (MAPbBr3)) nanocrystals embedded in terpolymer polyvinylidene fluoride trifluoroethylene-chlorofluoroethylene (PVDF-TrFE-CEF).
The MAPbBr3 perovskite material is a strong absorber of visible light, while PVDF-TrFE-CEF has a high dielectric constant. "We chose hybrid perovskites because of their exceptional photoelectronic properties, such as excellent light absorption, long carrier lifetime and high carrier mobility," explained Mani Teja Vijjapu from KAUST.
Tests with a 4x4 array and LED illumination of different visible colors indicate that the optical response of the array mimics the response of the human eye with a maximum sensitivity to green light. Importantly, the photoreceptors are also found to be highly stable, with no change in response even after being stored for 129 weeks in ambient conditions.
Future plans for the team include building larger arrays of photoreceptors, optimizing the interface circuit design and employing a multilayered neural network to improve the accuracy of the recognition functionality.