# Optical Convolutional Neural Networks: Methodology and Advances (Invited)
## 2 Development of Optical Convolution Neural Network
### Optical CNN Based on Wavelength Division Multiplexing
Using the multi-dimensional multiplexing of light waves, including wavelength, space, and time, high-order tensors can be directly processed in the optical domain. The integrated photonics tensor core on phase change materials is demonstrated in Figure 7c [39]. In this scheme, phase change materials work as wavelength-independent variable optical amplitude modulators to enable efficient calculation without power to maintain the state. A chip-based optical frequency comb was introduced as the light source to perform high parallelism optical convolution processing with a speed of 2 tera-MAC operations per second. The experimental recognition accuracy on 10,000 images in the MNIST test dataset was up to 95.3%. A phase-change metasurface mode converter (PMMC) was also fabricated to form an optical CNN with 6-bit weight precision [104]. The waveguide of two spatial modes was controlled by the phase change material-based phase-gradient metasurface, and 2 \(\times\) 2 real-valued kernels were experimentally demonstrated. Figure 7: WDM-based optical CNN realizations. **(a)** The principle of digital electronics and analog photonics (DEAP) architecture CNNs with MRRs as the variable optical amplitude modulators (VOAMs) [97]. **(b)** Integrated photonic tensor flow processor (PTFP) based on MRRs [101]. **(c)** The first realization of a photonics tensor core with phase change material (PCM) as the VOAM [39]. **(d)** Photonic convolution accelerator [35]. **(e)** On-demand reconfigurable optical matrix operator [106]. **(f)** Optical realization of binary convolution based on a dual-drive Mach–Zender (DD-MZM) modulator [110]. (**g**) MRR-based photonic processing unit (PPU) [36]. (**a**) Reprinted with permission from Ref. [97]. 2020, IEEE. (**b**,**f**,**g**) Reprinted with permission from Refs. [36; 101; 110] under a CC BY 4.0 license. (**c**) and (**d**) Reprinted with permission from Refs. [35; 39]. 2021, Springer Nature. (**e**) Reprinted with permission from Ref. [106]. 2023, IEEE. Otherwise, a parallel photonics convolution accelerator with ten 3 \(\times\) 3 kernels in Figure 7d is illustrated using a soliton optical frequency comb as the light source to operate with a speed of 11 TOPS [35]. Herein, the 2D image data are flattened into a 1D vector and modulated to multiple wavelengths to achieve electro-optical conversion in the single intensity modulator with the serial data input approach. The weight matrix is represented by the amplitude of optical combs and adjusted with a waveshaper. The proposed photonic convolution accelerator cooperates with a fully connected optical layer to classify handwritten digits with an experimental accuracy of 88% on 50 images. With the homologous function (Figure 7e), laser arrays are used to replace the optical frequency comb to improve the robustness of the computing system and obtain an experimental recognition accuracy of 96.01% [106]. The on-demand real-time reconfiguration between the optical perceptron and optical convolution operator is also experimentally demonstrated. As illustrated in Figure 7f, the binary optical convolution operation [110] is also realized by controlling the dual-drive Mach-Zehnder modulators to work at two quadrature points, where the input data and the weights simultaneously drive two arms of the modulator and an optical fiber is occupied for dispersive delay. In 2023, a compact MRR-based photonic processing unit was reported (Figure 7g) with all the essential photonic components integrated, and a photonic-core compute density of over 1 TOPS/mm\({}^{2}\) was achieved [36]. Using the through port of the MRR as the monitor port, a weight precision of 9 bit was realized. Negative elements in kernels were accomplished via subtraction between two channels, and a handwritten digit recognition accuracy of 96.6% was experimentally illustrated, which is comparable with a digital computer.