# Numerical Simulation of an InP Photonic Integrated Cross-Connect for Deep Neural Networks on [PERSON] Shi
## 3 Optical Cross-Connect: Implementation and Simulation  
We use _VPIphotonics_ to simulate the integrated cross-connect-based weighted addition as the basic function of the photonic deep neural network. This software allows for numerical modeling of photonic systems as well as of photonic components within the integrated chips and for different material platforms. The simulated set up is built with symbolic blocks and a hierarchal structure. For the passive elements, we execute the simulation in frequency domain, while for the active elements, such as the SOAs, the transmission-line model is applied to model them in time domain [32].  
The implemented and simulated setup scheme is showed in Figure 2. Figure 2a is the complete setup scheme for examining our cross-connect photonic integrated chip shown in Figure 1c, with similar operating conditions as in the real experiment, for analyzing the integrated SOA-based PDNN. The photonic integrated chip is an \(8\times 8\times 8\lambda\) cross-connect, but in the experiments, a WDM input is used which contains 4 channels. An arbitrary waveform generator (detailed scheme shown in Figure 2b) is utilized to generate the electrical signal from the data file at 10 GSymbol/s, with 4 DACs with 8-bit precision. Figure 2c shows 4 lasers and 4 modulators for the optical signal generation of 4 input channels. The WDM input of four channels is generated via these four Mach-Zehnder interferometer-based modulators, with the electrical RF signal coming from the arbitrary waveform generator, and CW lasers at 193.1 THz, 193.5 THz, 193.9 THz, and 194.3 THz. A channel separation of 3.2 nm is used to match the channel separation of the AWG on chip. The input signal is coupled into the photonic matrix multiplication unit (MMU) with a 0 dBm optical input peak power for each channel. The output of the MMU is coupled to the receiver, shown in Figure 2d, which consists of a pre-amplifier with a noise figure of 5.0 dB, an AC-coupled (i.e., with DC-removing block in the simulation) 10 GHz avalanche photodetector (APD), and an analog-digital converter (ADC). The output from the MMU is then coupled to a 0.08 nm optical passband filter to monitor the peak power of one single channel at the output. The details of the schematic of part of the photonic MMU, i.e., the weighted addition unit, are illustrated in Figure 2e for the weighted addition demonstration. This will be used as the weighted addition part within a three-layer PDNN for demonstrating the iris flower classification. The path loss is the attenuation of the optical signal happening along the waveguide. The input signal is amplified with a pre-SOA and is split into 8 as for 8 neurons. Firstly, we study the performance of one neuron so that only one path carrying one WDM input signal is connected to the next SOA, the input vector selection SOA, that acts as a port selector as shown in Figure 1b. The WDM signal is then demultiplexed by an AWG, and the individual channel is weighted by the weight-SOA, and combined at the output of the unit. The parameters used in the simulation for the SOAs are listed in Table 1. The results are reported and explained, as related to the weight calibration and the weighted addition (Section 4), and the Iris classification application (Section 5) together with the analysis of the impact of the optical crosstalk and the optical path loss.