Kun Liao, Ye Chen, Zhongcheng Yu, Xiaoyong Hu, Xingyuan Wang, Cuicui Lu, Hongtao Lin, Qingyang Du, Juejun Hu, Qihuang Gong. All-optical computing based on convolutional neural networks[J]. Opto-Electronic Advances, 2021, 4(11): 200060-1

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- Opto-Electronic Advances
- Vol. 4, Issue 11, 200060-1 (2021)

Fig. 1. General architecture of the all-optical computing framework. (a ) The CNN architecture showing the connections between adjacent layers:
, where
and
represent the l th and (l −1)th layer’s optical signal respectively,
shows the weight that determines how signals are propagated from l th layer to (l −1)th layer. (b ) Schematic diagram of the all-optical transcendental equation solver. (c ) Top-view SEM image of the all-optical transcendental equation solver, where the scale bar is 100 μm. Here, the white dotted lines mark the five layers for waveform discretization, and the red dotted lines separate the three layers of the optical CNN structure.

Fig. 2. Weight regulation. (a ) SEM image of Y-shaped waveguides side-coupled silicon weight modulators. Two arms of the “Y” structure waveguide correspond to two kinds of weights. By regulating the length a of weight modulator and the gap width b between two waveguides: (b ) The magnitude of weight ω can be continuously adjusted from 0.03 to 0.95. (c ) The phase of weight ω can be continuously adjusted from 0 to 2π.

Fig. 3. All-optical transcendental equation solver. (a ) Output light intensity distribution in the output waveguides (k = 1.67). The arrows in the figure correspond to the locations of the solutions. The horizontal axis is the number of discrete waveguides, the vertical axis on the left represents the output signal intensity, and the vertical axis on the right gives the deviation between the experimental output signal and the theoretical value. (b ) A graphic representation of solution deviation. The horizontal axis labels the individual solutions, and the vertical axis represents three values of the parameter k . The shade of the color indicates the magnitude of the deviation.

Fig. 4. Multifarious logic gates. (a ) Schematic diagram of the multifarious logic gate operator. Ports A , B are the signal inputs, and ports C 1, C 2, C 3, C 4, together constitute the control bits, and Y represents the signal output. (b ) Top-view SEM image of the multifarious logic gate operator. (c ) 0−1 intensity distribution when the optical CNN device acts as three different types of logic gates. (d ) Overlay of three logic function responses in the optical CNN structure. The top red line corresponds to the minimum intensity of “1”, and the bottom red line shows the maximum intensity of “0”.

Fig. 5. Half-adder. (a ) Schematic diagram of the half-adder. Ports A , B are the signal inputs, and C and S represent the Carry and the Sum bit, respectively. (b ) Top-view SEM image of the half-adder. (c ) Intensity distribution of Sum bit and Carry bit corresponding to three different input signals in the half-adder. The blue lines give the average intensity values of the 0 and 1 logic states.

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