AI 中文总结
研究针对传统计算架构局限，探讨铁离子二维材料在可编程光子学中的作用，利用其材料动力学实现多级光子态等，通过后端集成策略引入多功能性，为可重构等光子计算架构提供途径，助力神经形态和自适应光子系统发展。
AI 中文摘要
人工智能模型迅速扩展，暴露出传统计算架构的根本局限性，即内存与计算的物理分离带来大量能量和延迟开销。光学神经网络通过光的内在并行性实现高通量、低延迟计算，提供了可行方案。然而，光子计算的可扩展性受限于缺乏能提供低损耗、节能且非易失性光学相位控制的材料。本文讨论铁离子二维材料作为可编程光子学平台的新作用。与传统铁电体不同，铁离子系统能实现场驱动离子重新分布，可利用其材料动力学实现多级光子态和非易失性相位控制。还强调了一种后端集成策略，能在不影响光学性能的情况下为预制光子电路引入多功能性。铁离子材料为可重构、低损耗和节能的光子计算架构提供了途径，为神经形态和自适应光子系统带来新机遇。
英文摘要
Artificial intelligence (AI) models are scaling rapidly, exposing fundamental limitations in conventional computing architectures, where the physical separation of memory and computation imposes substantial energy and latency overheads. Optical neural networks offer a viable solution by enabling high-throughput, low-latency computation through the intrinsic parallelism of light. However, the scalability of photonic computing is constrained by the lack of materials that can provide low-loss, energy-efficient, and nonvolatile control of optical phase. Here, we discuss the emerging role of ferroionic two-dimensional materials as a platform for programmable photonics. Unlike conventional ferroelectrics, where polarization arises from bounded lattice distortions, ferroionic systems enable field-driven ionic redistribution, providing access to a continuum of stable and reconfigurable states. We outline how these material dynamics can be exploited to realize multi-level photonic states and nonvolatile phase control. Furthermore, we highlight a back-end integration strategy that introduces multifunctionality, including tunable optical nonlinearities into pre-fabricated photonic circuits without compromising optical performance. By bridging material-level dynamics with device- and system-level functionality, ferroionic materials provide a pathway toward reconfigurable, low-loss, and energy-efficient photonic computing architectures, opening new opportunities for neuromorphic and adaptive photonic systems.
