分类: 光学 >> 量子光学 提交时间: 2023-02-19
摘要: Electrical tuning of second-order nonlinearity in optical materials is attractive to strengthen and expand the functionalities of nonlinear optical technologies, though its implementation remains elusive. Here, we report the electrically tunable second-order nonlinearity in atomically thin ReS2 flakes benefiting from their distorted 1T crystal structure and interlayer charge transfer. Enabled by the efficient electrostatic control of the few-atomic-layer ReS2, we show that second harmonic generation (SHG) can be induced in odd-number-layered ReS2 flakes which are centrosymmetric and thus without intrinsic SHG. Moreover, the SHG can be precisely modulated by the electric field, reversibly switching from almost zero to an amplitude more than one order of magnitude stronger than that of the monolayer MoS2. For the even-number-layered ReS2 flakes with the intrinsic SHG, the external electric field could be leveraged to enhance the SHG. We further perform the first-principles calculations which suggest that the modification of in-plane second-order hyperpolarizability by the redistributed interlayer-transferring charges in the distorted 1T crystal structure underlies the electrically tunable SHG in ReS2. With its active SHG tunability while using the facile electrostatic control, our work may further expand the nonlinear optoelectronic functions of two-dimensional materials for developing electrically controllable nonlinear optoelectronic devices.
分类: 光学 >> 量子光学 提交时间: 2023-02-19
摘要: Photonic neuromorphic computing has emerged as a promising avenue toward building a low-latency and energy-efficient non-von-Neuman computing system. Photonic spiking neural network (PSNN) exploits brain-like spatiotemporal processing to realize high-performance neuromorphic computing. However, the nonlinear computation of PSNN remains a significant challenging. Here, we proposed and fabricated a photonic spiking neuron chip based on an integrated Fabry-P\'erot laser with a saturable absorber (FP-SA) for the first time. The nonlinear neuron-like dynamics including temporal integration, threshold and spike generation, refractory period, and cascadability were experimentally demonstrated, which offers an indispensable fundamental building block to construct the PSNN hardware. Furthermore, we proposed time-multiplexed spike encoding to realize functional PSNN far beyond the hardware integration scale limit. PSNNs with single/cascaded photonic spiking neurons were experimentally demonstrated to realize hardware-algorithm collaborative computing, showing capability in performing classification tasks with supervised learning algorithm, which paves the way for multi-layer PSNN for solving complex tasks.