The exponential growth of information stored in data centers and computational power required for various data-intensive applications, such as deep learning and AI, call for new strategies to improve or move beyond the traditional von Neumann architecture. Recent achievements in information storage and computation in the optical domain, enabling energy-efficient, fast, and high-bandwidth data processing, show great potential for photonics to overcome the von Neumann bottleneck and reduce the energy wasted to Joule heating. Optically readable memories are fundamental in this process, and while light-based storage has traditionally (and commercially) employed free-space optics, recent developments in photonic integrated circuits (PICs) and optical nano-materials have opened the doors to new opportunities on-chip. Photonic memories have yet to rival their electronic digital counterparts in storage density; however, their inherent analog nature and ultrahigh bandwidth make them ideal for unconventional computing strategies. Here, we review emerging nanophotonic devices that possess memory capabilities by elaborating on their tunable mechanisms and evaluating them in terms of scalability and device performance. Moreover, we discuss the progress on large-scale architectures for photonic memory arrays and optical computing primarily based on memory performance.

中文翻译：

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光子（计算）存储器：用于数据存储和计算的可调纳米光子学

存储在数据中心的信息和深度学习和人工智能等各种数据密集型应用所需的计算能力呈指数增长，需要新的策略来改进或超越传统的冯诺依曼架构。最近在光学领域的信息存储和计算方面取得的成就，实现了高能效、快速和高带宽的数据处理，显示出光子学在克服冯诺依曼瓶颈和减少焦耳加热浪费的能量方面的巨大潜力。光学可读存储器是这一过程的基础，虽然基于光的存储传统上（和商业上）采用自由空间光学，但光子集成电路 (PIC) 和光学纳米材料的最新发展为新机遇打开了大门——芯片。光子存储器在存储密度方面尚未与电子数字存储器相媲美。然而，它们固有的模拟特性和超高带宽使它们成为非常规计算策略的理想选择。在这里，我们通过详细说明其可调机制并在可扩展性和设备性能方面评估它们来回顾具有存储能力的新兴纳米光子设备。此外，我们讨论了主要基于内存性能的光子内存阵列和光学计算的大规模架构的进展。我们通过详细说明其可调机制并在可扩展性和设备性能方面评估它们来审查具有存储能力的新兴纳米光子设备。此外，我们讨论了主要基于内存性能的光子内存阵列和光学计算的大规模架构的进展。我们通过详细说明其可调机制并在可扩展性和设备性能方面评估它们来审查具有存储能力的新兴纳米光子设备。此外，我们讨论了主要基于内存性能的光子内存阵列和光学计算的大规模架构的进展。