分类： 光学 >> 量子光学 提交时间： 2023-02-19
摘要： Novel physical concepts that originate from quantum mechanics, such as non-Hermitian systems (dealing mostly with PT and anti-PT symmetry) and bound states in the continuum (BICs), have attracted great interest in the optics and photonics community. To date, BICs and anti-PT symmetry seem to be two independent topics. Here, we for the first time propose a parallel cascaded-resonator system to achieve BICs and anti-PT symmetry simultaneously. We found that the requirements for the Fabry-P\'erot BIC and anti-PT symmetry can both be satisfied when the phase shift between any two adjacent resonators is an integer multiple of {\pi}. We further analyzed the cascaded-resonator systems which consist of different numbers of resonators and demonstrated their robustness to fabrication imperfections. The proposed structure can readily be realized on an integrated photonic platform, which can have many applications that benefit from the advantages of both BICs and anti-PT symmetry, such as ultralow-linewidth lasing, enhanced optical sensing, and optical signal processing.
分类： 光学 >> 量子光学 提交时间： 2023-02-19
摘要： The optical neural network (ONN) has been considered as a promising candidate for next-generation neurocomputing due to its high parallelism, low latency, and low energy consumption, with significant potential to release unprecedented computational capability. Large-scale ONNs could process more neural information and improve the prediction performance. However, previous ONN architectures based on matrix multiplication are difficult to scale up due to manufacturing limitations, resulting in limited scalability and small input data volumes. To address this challenge, we propose a compact and scalable photonic computing architecture based on temporal photoelectric multiplication and accumulate (MAC) operations, allowing direct processing of large-scale matrix computations in the time domain. By employing a temporal computing unit composed of cascaded modulators and time-integrator, we conduct a series of proof-of-principle experiments including image edge detection, optical neural networks-based recognition tasks, and sliding-window method-based multi-target detection. Thanks to its intrinsic scalability, the demonstrated photonic computing architecture could be easily integrated on a single chip toward large-scale photonic neural networks with ultrahigh computation throughputs.
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