We demonstrate a passive all-chalcogenide all-optical perceptron scheme. The network’s nonlinear activation function (NLAF) relies on the nonlinear response of Ge2Sb2Te5 to femtosecond laser pulses. We measured the sub-picosecond time-resolved optical constants of Ge2Sb2Te5 at a wavelength of 1500 nm and used them to design a high-speed Ge2Sb2Te5-tuned microring resonator all-optical NLAF. The NLAF had a sigmoidal response when subjected to different laser fluence excitation and had a dynamic range of −9.7 dB. The perceptron’s waveguide material was AlN because it allowed efficient heat dissipation during laser switching. A two-temperature analysis revealed that the operating speed of the NLAF is ≤ 1 $\le 1$ ns. The percepton’s nonvolatile weights were set using low-loss Sb2S3-tuned Mach Zehnder interferometers (MZIs). A three-layer deep neural network model was used to test the feasibility of the network scheme and a maximum training accuracy of 94.5% was obtained. We conclude that combining Sb2S3-programmed MZI weights with the nonlinear response of Ge2Sb2Te5 to femtosecond pulses is sufficient to perform energy-efficient all-optical neural classifications at rates greater than 1 GHz.

中文翻译：

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基于硫族化物的可编程全光深度神经网络

我们展示了一种无源全硫属化物全光感知器方案。网络的非线性激活函数 (NLAF) 依赖于 Ge 的非线性响应2锑2碲5飞秒激光脉冲。我们测量了 Ge 的亚皮秒时间分辨光学常数2锑2碲5波长为 1500 nm 并用它们设计了高速 Ge2锑2碲5调谐微环谐振器全光 NLAF。当受到不同的激光能量激发时，NLAF 具有 S 型响应，动态范围为 -9.7 dB。感知器的波导材料是 AlN，因为它可以在激光切换期间有效散热。两个温度分析表明，NLAF 的运行速度为 ≤ 1 $\le 1$ ns。感知器的非易失性权重使用低损耗 Sb 设置2小号3- 调谐马赫曾德尔干涉仪 (MZI)。采用三层深度神经网络模型测试该网络方案的可行性，最大训练准确率为94.5%。我们得出结论，结合 Sb2小号3-具有 Ge 非线性响应的编程 MZI 权重2锑2碲5到飞秒脉冲足以以大于 1 GHz 的速率执行节能的全光神经分类。