Bei Chen, Zhaoyang Zhang, Tingge Dai, Hui Yu, Yuehai Wang, Jianyi Yang. Photonic Neural Networks and Its Applications[J]. Laser & Optoelectronics Progress, 2023, 60(6): 0600001
- Laser & Optoelectronics Progress
- Vol. 60, Issue 6, 0600001 (2023)
![The development of artificial intelligence. (a) Typical architectures of ANN[2]; (b) comparison between total amount of AI compute and Moore's law[10]](/richHtml/lop/2023/60/6/0600001/img_01.jpg)
Fig. 1. The development of artificial intelligence. (a) Typical architectures of ANN[2]; (b) comparison between total amount of AI compute and Moore's law[10]
Fig. 2. Optical computing. (a) Classification of optical computing; (b) implementations of optical computing
![Evolution of PNNs[51,73-74,85,93-94,113-123] during the development of ANNs[97-112]](/Images/icon/loading.gif){kind=icon}
Fig. 4. Optical linear matrix multiplication. (a) Free-space optics; (b) integrated coherent optics; (c) WDM optics
Fig. 5. Optical linear matrix multiplications based on free-space optics. (a)-(c) SLM[76,127-128]; (d) (e) DMD[85,129]; (f)-(h) diffractive optics[74,80,130]
Fig. 6. Optical linear matrix multiplications based on integrated coherent optics. (a)-(d) Programmable MZI arrays[73,82-83,133]; (e) configurable push-pull modulators[134]; (f) combination of on-chip diffractive cell and programmable MZI[95]
Fig. 7. Optical linear matrix multiplications based on WDM optics. (a) (b) The cascaded MRRs[46,89,122]; (c)-(e) PCMs[51,81,92]; (f) SOA[136]; (g) dispersion fiber[137]; (h) (i) optical frequency combs[93-94]
Fig. 8. Typical expressions of nonlinear activation functions in ANNs. (a) Sigmoid; (b) Tanh; (c) Relu; (d) Leaky Relu; (e) Softpuls; (f) Swish
Fig. 9. Optical nonlinear activators based on O-E-O conversion. (a)(b) Electro-optic modulators[93-94]; (c) MRR modulator[93-94];(d) feedback-assisted MZI[143]
Fig. 10. All-optical nonlinear activators and the corresponding response curves. (a)-(f) Custom-defined materials[51,76,123,144-145]; (g)-(i) SOAs[146-148]; (j)-(l) MRRs[149-152]
Fig. 11. Design process of photonic neural networks
Fig. 12. Typical applications of photonic neural networks. (a)-(e) Image processing or recognition[82,92-94,134,136]; (f) vowel recognition[73]; (g) OAM multiplexing and demultiplexing[153]; (h) logic operation[80]; (i) fiber nonlinearity compensation[154]
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Table 1. Comparison among different implementations of optical linear matrix multiplication
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