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arXiv 2201.00225physics.opticscond-mat.mes-hall
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基于激子-极化激元超快时延效应的神经网络

Neural networks based on ultrafast time-delayed effects in exciton-polaritons

Rafał Mirek, Andrzej Opala, Magdalena Furman, Mateusz Król, Krzysztof Tyszka, Bartłomiej Seredyński, Wojciech Pacuski, Jan Suffczyński, Jacek Szczytko, Michał M… 展开作者

Rafał Mirek, Andrzej Opala, Magdalena Furman, Mateusz Król, Krzysztof Tyszka, Bartłomiej Seredyński, Wojciech Pacuski, Jan Suffczyński, Jacek Szczytko, Michał Matuszewski, Barbara Piętka

摘要:

我们证明了激子极化激元中的时间延迟非线性效应可以用来构建神经网络,其中信息被编码在连续到达样品的光脉冲中。与激子库的时间依赖性相互作用引起的高度非线性效应。这些非线性允许创建可以在皮秒时间尺度上执行操作的非线性XOR逻辑门。基于所构造的逻辑门的光电神经网络以高准确率对语音数字进行分类。

英文摘要:

We demonstrate that time-delayed nonlinear effects in exciton-polaritons can be used to construct neural networks where information is coded in optical pulses arriving consecutively on the sample. The highly nonlinear effects are induced by time-dependent interactions with the excitonic reservoir. These nonlinearities allow to create a nonlinear XOR logic gate that can perform operations on the picosecond timescale. An optoelectronic neural network based on the constructed logic gate performs classification of spoken digits with a high accuracy rate.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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