Advances in artificial intelligence have greatly increased demand for data-intensive computing. Integrated photonics is a promising approach to meet this demand in big-data processing due to its potential for wide bandwidth, high speed, low latency, and low-energy computing. Photonic computing using phase-change materials combines the benefits of integrated photonics and co-located data storage, which of late has evolved rapidly as an emerging area of interest. In spite of rapid advances of demonstrations in this field on both silicon and silicon nitride platforms, a clear pathway towards choosing between the two has been lacking. In this paper, we systematically evaluate and compare computation performance of phase-change photonics on a silicon platform and a silicon nitride platform. Our experimental results show that while silicon platforms are superior to silicon nitride in terms of potential for integration, modulation speed, and device footprint, they require trade-offs in terms of energy efficiency. We then successfully demonstrate single-pulse modulation using phase-change optical memory on silicon photonic waveguides and demonstrate efficient programming, memory retention, and readout of $ \gt {4}$ bits of data per cell. Our approach paves the way for in-memory computing on the silicon photonic platform.

中文翻译：

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用于相变光子内存计算的硅和氮化硅平台的实验研究

人工智能的进步极大地增加了对数据密集型计算的需求。集成光子学因其宽带宽，高速，低延迟和低能耗计算的潜力而成为满足大数据处理需求的一种有前途的方法。使用相变材料的光子计算结合了集成光子学和位于同一地点的数据存储的优点，近来随着新兴的兴趣领域迅速发展。尽管在该领域的硅和氮化硅平台上的演示都取得了快速进展，但仍缺乏在两者之间进行选择的明确途径。在本文中，我们系统地评估和比较了硅平台和氮化硅平台上相变光子的计算性能。我们的实验结果表明，尽管硅平台在集成潜力，调制速度和设备占用空间方面要优于氮化硅，但它们在能量效率方面需要权衡取舍。然后，我们成功地演示了在硅光子波导上使用相变光学存储器的单脉冲调制，并演示了有效的编程，存储器保留和存储器的读出。$ \ gt {4} $每个单元格的数据位。我们的方法为在硅光子平台上进行内存计算铺平了道路。