论文标题
基于超快延误的神经网络在激子 - 孔子中的效果
Neural networks based on ultrafast time-delayed effects in exciton-polaritons
论文作者
论文摘要
我们证明，激子 - 孔子中的时间删除的非线性效应可用于构建神经网络，其中信息在样品上连续到达的光脉冲中编码。高度非线性效应是由时间依赖性与激子储层的相互作用引起的。这些非线性允许创建一个非线性XOR逻辑门，该门可以在Picsecond TimeScale上执行操作。基于构造的逻辑门的光电神经网络以高精度速率对口语数字进行分类。
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.
