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Patterned multimode interference coupler based on light scattering, design method and application

A multi-mode interference and coupler technology, applied in the coupling of optical waveguides, light guides, optics, etc., can solve the problems of reduced application, narrow bandwidth, and large volume, and achieve the effect of avoiding processing steps and reducing size.

Active Publication Date: 2021-04-02
WESTLAKE UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

To date, the goal of achieving small-scale incoherent optical neural network architectures on-chip remains elusive
[0006] In addition, most of the existing couplers are couplers based on the MZI structure to achieve interference. The couplers with this type of structure have the following defects: first, they are relatively sensitive to temperature, which greatly reduces their application in occasions with large temperature changes; The bandwidth is narrow, and the third is that the volume is relatively large, which increases the overall complexity of the device. The structure based on the MMI coupler can solve the above problems

Method used

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  • Patterned multimode interference coupler based on light scattering, design method and application
  • Patterned multimode interference coupler based on light scattering, design method and application
  • Patterned multimode interference coupler based on light scattering, design method and application

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Embodiment 1

[0052] Figure 3A An optical neural network architecture based on a multilayer MMI coupler is shown. Including N×NMMI coupler contains N input waveguides and N output waveguides, plus a multimode coupler in the middle. In this embodiment, the multimode coupler directly adopts the overall structure of the waveguide material. In order to provide N×N trainable parameters for the optical neural network, m MMI couplers (m=N+1) are sequentially cascaded together, and N phase shifters are inserted between the two MMI couplers, such as Figure 3A shown. Calculate the transmission matrix M of the unit n can be achieved by combining alternating couplers and phase shifters, for M n =(M coupler M shift ) m-1 M coupler . Here m represents the number of MMI couplers. m coupler is a constant matrix where each entry represents the relative phase between the input and output waveguides. m shift is a trainable diagonal matrix, M shift Each term on the diagonal represents the phase...

Embodiment 2

[0057] Figure 4A A nano-patterned MMI coupler based on light scattering is shown, which can replace the multilayer optical interference calculation unit to realize optical linear transformation, and further reduce the size of the optical calculation unit.

[0058] Structurally speaking, the nano-patterned MMI coupler based on light scattering of the present invention includes a coupler body, and a plurality of input waveguides and a plurality of output waveguides respectively connected to the input and output ends of the coupler body, the coupling The device body includes: a supporting substrate; a silicon substrate arranged on the supporting substrate, and a plurality of irregular scattering voids extending along the height direction of the silicon substrate are arranged on the silicon substrate, and the distribution information and size information of these voids are determined by The best refractive index distribution results obtained during design are determined. For the...

Embodiment 3

[0062] Based on the light scattering unit, we propose an incoherent optical neural network architecture, including several optical neural network layers, each optical neural network layer includes an optical matrix multiplication unit and an optical nonlinear unit, wherein the optical matrix multiplication unit is implemented as The light scattering based patterned MMI coupler disclosed in Example 2. Using the above-mentioned incoherent optical neural network architecture, deep learning tasks such as matrix multiplication and image classification can be realized. The advantage of the incoherent photonic architecture is that matrix multiplication can be implemented based on light intensity rather than electric field. For light modulation or light detection, light intensity is easier to control than electric field due to the complex requirement of phase control. The incoherent light scattering unit can input optical signals of different wavelengths through different waveguides ...

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Abstract

The invention discloses a patterned multimode interference (MMI) coupler based on light scattering and a design method thereof, and application of a patterned multimode interference coupler based on light scattering. The patterned multimode interference coupler comprises a coupler body, a plurality of input waveguides and a plurality of output waveguides, wherein the input waveguides and the output waveguides are respectively connected to the input end and the output end of the coupler body. The coupler body comprises: a supporting substrate; a waveguide base body arranged on the supporting substrate; and a plurality of scattering gaps extending in the height direction of the waveguide base body formed in the waveguide base body, wherein the scattering gaps are arranged according to set refractive index distribution requirements. According to the patterned MMI coupler based on light scattering, the coupler can use only one MMI coupler to achieve matrix multiplication through construction of the patterned coupler structure to replace a traditional structure of overlapping of a plurality of couplers and a phase shifter so as to further reduce the size of an optical calculation unit and greatly simplify the structure of an optical network nervous system.

Description

technical field [0001] The invention relates to the hardware realization of an optical artificial intelligence chip, in particular to a patterned MMI coupler based on light scattering, a design method and an application. Background technique [0002] In recent years, deep learning has made great progress in areas such as image classification, speech recognition, and decision-making. In addition, deep learning has also penetrated into scientific fields, such as drug design, genetics, material science, and physics. In the face of complex problems and high precision, people need huge neural networks, which in turn require a lot of computing resources. Many high-speed and low-power electronic chips have been developed for this purpose, such as graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs), including the Google TPU. Additionally, new computing architectures such as neuromorphic computing and quantum...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G02B6/28
CPCG02B6/2813
Inventor 曲俞睿朱桓正仇旻
Owner WESTLAKE UNIV