记者获悉，该研究成果为突破传统自旋电子学的性能瓶颈、设计超低功耗器件提供了全新的物理原理和 设计思路。 宁波材料所研究员汪志明介绍，随着人工智能与大数据的发展，传统电子技术正逼近性能极限，"功耗 墙"成为制约技术发展的瓶颈。为此，科学家们将目光投向了自旋电子学这一前沿领域。新一代自旋电 子器件在理论上具备了高速、非易失等优势，并被视为突破"功耗墙"的潜力技术。 新华社杭州8月15日电（记者唐弢、郑可意）中国学者揭示了一项可显著降低自旋电子器件能耗的物理 机制。中国科学院宁波材料技术与工程研究所柔性磁电功能材料与器件团队发现，利用电子"轨道"属性 遵循的非传统标度律，能化电子运动阻力为性能增益。相关研究成果于北京时间8月15日在线发表于国 际学术期刊《自然-材料学》。 该研究结果表明，利用"反常标度律"，通过主动引入缺陷，能够实现轨道霍尔角和轨道霍尔电导的同时 增大，从而一举突破传统方法的限制，显著降低器件的写入电流和功耗。这一发现不仅为高效的轨道电 子学器件提供了新的物理基础，也为整个自旋电子学领域带来了全新的设计思路。（完） 然而，影响自旋电子器件"自旋流"产生效率的两个关键指标，即自旋霍尔角和自旋霍尔电导 ...

Core Viewpoint - The rapid development of large language models (LLMs) is pushing the limits of contemporary computing hardware, necessitating exploration of alternative computing paradigms such as photonic hardware to meet the increasing computational demands of AI models [1][4]. Group 1: Photonic Hardware and Its Advantages - Photonic computing utilizes light for information processing, offering high bandwidth, strong parallelism, and low thermal dissipation, which are essential for next-generation AI applications [4][5]. - Recent advancements in photonic integrated circuits (PICs) enable the construction of fundamental neural network modules, such as coherent interferometer arrays and micro-ring resonator weight arrays, facilitating dense matrix multiplication and addition operations [4][5]. - The integration of two-dimensional materials like graphene and transition metal dichalcogenides (TMDCs) into silicon-based photonic platforms enhances the functionality of modulators and on-chip synaptic elements [5][31]. Group 2: Challenges in Mapping LLMs to New Hardware - Mapping transformer-based LLM architectures to new photonic hardware presents challenges, particularly in designing reconfigurable circuits for dynamic weight matrices that depend on input data [5][6]. - Achieving nonlinear functions and normalization in photonic or spintronic media remains a significant technical hurdle [5][6]. Group 3: Key Components and Technologies - Photonic neural networks (PNNs) leverage various optical devices, such as micro-ring resonators and Mach-Zehnder interferometer arrays, to perform efficient computations [9][13]. - The use of metasurfaces allows for high-density parallel optical computations by modulating light properties through sub-wavelength structured materials [14][16]. - The 4f optical systems enable linear filtering functions through Fourier transformation, integrating deep diffraction neural networks into optical architectures [20][21]. Group 4: Integration of Two-Dimensional Materials - The integration of graphene and TMDCs into photonic chips is crucial for developing high-speed and energy-efficient AI hardware, with applications in optical modulators, photodetectors, and waveguides [31][35][36]. - Graphene's exceptional optical and electronic properties, combined with TMDCs' tunable bandgap, enhance the performance of photonic devices, making them suitable for AI workloads [31][32]. Group 5: Future Directions and Challenges - The scalability of integrating two-dimensional materials poses challenges due to their fragility, necessitating advancements in transfer techniques and wafer-scale synthesis [45]. - Material stability and the complexity of integration with existing CMOS processes are critical factors that need to be addressed for widespread adoption of these technologies [45][46].