Lithography-free integrated photonics for reconfigurable computing acceleration

We present a new lithography-free integrated photonic processor, targeting dynamic control of spatial-temporal modulations of the imaginary index on an active semiconductor platform. Leveraging its real-time reconfigurability, we aim to realize photonic neural networks with extraordinary flexibility to perform in-situ learning and training with high accuracy. Our work delivers a brand new and ultra-flexible integrated photonic paradigm for reconfigurable networking and computing, with great potential to process large, non-local datasets with high throughputs.