Thermo‐optic (TO) phase shifters are very fundamental units in large‐scale active silicon photonic integrated circuits (PICs). However, due to the limitation of microheater materials with a trade‐off between heating efficiency and absorption loss, designs reported so far typically suffer from slow response time, high power consumption, low yields, and so on. Here, an energy‐efficient, fast‐responding, low‐loss TO phase shifter is demonstrated by introducing hydrogen‐doped indium oxide (IHO) films as the microheater, and the optimized electron concentration with enhanced mobility endows the IHO with high conductivity as well as high near‐infrared (NIR) transparency, which allow it to directly contact the silicon waveguide without any insulating layer for efficient tuning and fast response. The TO phase shifter achieves a sub‐microsecond response time (970 ns/980 ns) with a π phase shift power consumption of 9.6 mW, and the insertion loss introduced by the IHO microheater is ≈0.5 dB. The proposed IHO‐based microheaters with compatible processing technology illustrate the great potential of such material in the application of large‐scale silicon PICs.
Optical nonlinear activation function is an indispensable part of the optical neural network. While linear matrix computation has thrived in an integrated optical neural network, there are many challenges for nonlinear activation function on a chip such as large latency, high power consumption and high threshold. Here, we demonstrate that Ge/Si hybrid structure would be a qualified candidate owing to its property of CMOS-compatibility, low nonlinear threshold and compact footprint. Thanks to the strong thermal-optic effect of germanium in conjunction with micro-ring resonator, we experimentally demonstrate three different types of nonlinear function (Radial basis, Relu and ELU functions) with a lowest threshold of 0.74 mW among our measured nonlinear functions and they can work well with a repetition rate below 100 kHz. Simultaneous size shrinkage of germanium and resonance constraint inside germanium is proposed to speed up response time. Furthermore, we apply our measured nonlinear activation function to the task of classification of MNIST handwritten digit image dataset and improve the test accuracy from 91.8% to 94.8% with feedforward full-connected neural network containing three hidden layers. It proves that our scheme has potential in the future optical neural network.
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