摘要： To respond the growing bandwidth demand by emerging applications such as fixed-mobile convergence for 5G and beyond 5G, 100Gb/s/λ access network becomes the next research focus of passive optical network (PON) roadmap. Intensity modulation and direct detection (IMDD) technology is still considered as a promising candidate for 100Gb/s/λ PON attributed to its low cost, low power consumption and small footprint. In this paper, we achieve 100Gb/s/λ IMDD PON by using 20G-class optical and electrical devices due to its commercial linear and nonlinear availability. To mitigate the system distortions, neural network (NN) based equalizer is used and the performance is compared with feedforward equalizer (FFE) and Volterra nonlinear equalizer (VNE). We introduce the rules to train and test the data when using NN-based equalizer to guarantee a fair comparison with FFE and VNE. Random data has to be used for training, but for test, both random data and psudo-random bit sequence (PRBS) are applicable. We found NN-based equalizer has the same performance with FFE and VNE in the case of linear distortion only, but outperforms them in strong nonlinearity case. In the experiment, to improve the loss budget, we increase the launch power to 18 dBm, achieving a 30-dB loss budget for 33Gbaud/s PAM8 signal at the system frequency response of 16.2 GHz, attributed to the strong nonlinear equalization capability of NN.