Abstract
The challenges of developing neuromorphic vision systems inspired by the human eye come not only from how to recreate the flexibility, sophistication, and adaptability of animal systems, but also how to do so with computational efficiency and elegance. Similar to biological systems, these neuromorphic circuits integrate functions of image sensing, memory and processing into the device, and process continuous analog brightness signal in real-time. High-integration, flexibility and ultra-sensitivity are essential for practical artificial vision systems that attempt to emulate biological processing. Here, we present a flexible optoelectronic sensor array of 1024 pixels using a combination of carbon nanotubes and perovskite quantum dots as active materials for an efficient neuromorphic vision system. The device has an extraordinary sensitivity to light with a responsivity of 5.1x10(7)A/W and a specific detectivity of 2x10(16) Jones, and demonstrates neuromorphic reinforcement learning by training the sensor array with a weak light pulse of 1 mu W/cm(2). To emulate nature biological processing, highly-integrated ultra-sensitive artificial neuromorphic system is highly desirable. Here, the authors report flexible sensor array of 1024 pixels using combination of carbon nanotubes and perovskite QDs as active matetials, achieving highly responsive device for reinforcement learning.