To　alleviate　the　problems　of　long　training　time　and　computational　resource　consumption　in　diffractive　deep　neural　networks　(D　2　NNs),　we　present　the　2bit　nonlinear　diffractive　deep　neural　network　(2bit　ND　2　NN)　model,　which　employs　the　concept　of　quantized　neural　networks.　2bit　ND　2　NN　converts　the　phase　of　each　diffraction　layer　pixel　from　continuous　to　discrete　values,　i.e.,　the　five　regions　of　0,　pi/2,　pi,　3　pi/2　and　2　pi.　We　use　the　formulas　for　phase　and　relative　thickness　to　determine　the　thickness　values　of　the　pixels.　Furthermore,　the　phase　and　amplitude　of　neurons　in　the　2bit　ND　2　NN　model　are　both　considered　as　learnable　parameters.　We　use　a　revised　formula　to　determine　the　size　of　neurons　in　the　model,　and　we　determine　the　diffraction　grating　spacing,　the　number　of　grating　layers,　the　pixel　size　and　the　pixel　counts　through　ablation　experiments.　Experimental　results　for　image　classification　indicate　that　the　highest　accuracy　achieved　by　2bit　ND　2　NN　on　the　MNIST　and　FashionMNIST　datasets　is　97.88　%　and　89.28　%,　respectively.