Seven Bit Nonvolatile Electrically Programmable Photonics Based on Phase-Change Materials for Image Recognition

With the rapid development of the Internet of Things, how to efficiently store, transmit, and process massive amounts of data has become a major challenge now. Optical neural networks based on nonvolatile phase change materials (PCMs) have become a breakthrough point due to their zero static power consumption, low thermal crosstalk, large-scale, and high efficiency. However, current photonic devices cannot meet the multilevel requirements in neuromorphic computing due to their limited storage capacity. Here, a new way for increasing storage capacity is paved from the perspective of modulation of the crystallization kinetics of PCMs. A more progressive transition from the amorphous to the crystalline states is achieved through the grain-refinement phenomenon induced by nitrogen (N) element doping in Ge2Sb2Te5 (GST), giving precise access to more multibit states. By integrating N-doped Ge2Sb2Te5 (N-GST) with a waveguide, a high-capacity nonvolatile photonic device enabling over 7 bits (similar to 222 levels) storage is achieved for the first time. The storage capacity is increased nearly by 7 times compared to the state-of-the-art device (similar to 32 levels). An optical convolutional neural network is successfully established for the MINIST handwritten digit recognition task by mapping synapse weight to the multiple optical levels, and a recognition accuracy of up to 96.5% is achieved. Our work provides a new strategy for the development of integrated photonic devices with multilevel and demonstrates enormous application potential in the field of large-scale photonic neural networks.