Optical Control of Adaptive Nanoscale Domain Networks

Adaptive networks can sense and adjust to dynamic environments to optimize their performance. Understanding their nanoscale responses to external stimuli is essential for applications in nanodevices and neuromorphic computing. However, it is challenging to image such responses on the nanoscale with crystallographic sensitivity. Here, the evolution of nanodomain networks in (PbTiO3)n/(SrTiO3)n superlattices (SLs) is directly visualized in real space as the system adapts to ultrafast repetitive optical excitations that emulate controlled neural inputs. The adaptive response allows the system to explore a wealth of metastable states that are previously inaccessible. Their reconfiguration and competition are quantitatively measured by scanning x‐ray nanodiffraction as a function of the number of applied pulses, in which crystallographic characteristics are quantitatively assessed by assorted diffraction patterns using unsupervised machine‐learning methods. The corresponding domain boundaries and their connectivity are drastically altered by light, holding promise for light‐programable nanocircuits in analogy to neuroplasticity. Phase‐field simulations elucidate that the reconfiguration of the domain networks is a result of the interplay between photocarriers and transient lattice temperature. The demonstrated optical control scheme and the uncovered nanoscopic insights open opportunities for the remote control of adaptive nanoscale domain networks. Ultrafast optical pulse trains chart new pathways to produce a wide range of polar nanostructures in PbTiO3/SrTiO3 superlattices, distinct from a supercrystal resulting from a strong single‐shot excitation. The reconfigurations of these nanostructures are captured by in situ synchrotron‐based x‐ray nanodiffraction imaging upon light excitation. This work sheds nanoscopic insights into light‐driven reconfigurable adaptive networks and opens the perspective of polar nanodomains in light‐programable, ferroelectric‐based synapse networks for neuromorphic computing.