相关实验视频
Updated: Jan 9, 2026

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Generation and Coherent Control of Pulsed Quantum Frequency Combs
Published on: June 8, 2018
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概括
我们开发了一个芯片规模的光子极端学习机器 (ELM),使用微空洞中的波浪混乱. 这种紧,节能硬件展示了先进人工智能任务的潜力.
科学领域:
- 光子学是指光子学的使用方法.
- 人工智能的人工智能
- 光学计算是指光学计算的应用.
背景情况:
- 对可扩展,节能的人工神经网络的日益增长的需求推动了对新型硬件的研究.
- 集成光子学提供了一个紧,并行和超快的平台,适合极端学习机器 (ELM) 架构.
研究的目的:
- 为了实验证明芯片规模的光子ELM.
- 为了利用信息处理的体育场微空洞中的波浪混乱干扰.
- 展示系统对不同计算任务的适应性.
主要方法:
- 输入信息是通过可调节激光源的波长编码的.
- 使用直接激光写字 (SU-8聚合物在玻璃上) 制造体育场微腔.
- 利用周围的散射墙作为泄漏模式的读取层.
主要成果:
- 从散射屏障中观察到非相关的和无周期的斑点图案.
- 在三个基准任务上证明了成功的分类性能.
- 通过调整分散屏障的读取区域来展示可调节的输出节点.
结论:
- 基于波浪混乱的光子ELM是人工智能的可行的硬件解决方案.
- 通过控制读出尺寸,可以优化系统的性能.
- 这种方法为节能光学计算提供了一个有希望的方向.
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