Diffractive　deep　neural　networks　(D2NNs)　typically　adopt　a　densely　cascaded　arrangement　of　diffractive　masks,　leading　to　multiple　reflections　of　diffracted　light　between　adjacent　masks,　thereby　affecting　the　network＇s　inference　capability.　It　is　challenging　to　fully　simulate　this　multiple-reflection　phenomenon.　To　eliminate　this　phenomenon,　we　designed　tilted-mode　all-optical　diffractive　deep　neural　networks　(T-D2NNs)　and　proposed　a　theoretical　model　for　diffraction　propagation　in　the　tilted　mode.　Simulation　results　indicate　that　T-D2NNs　address　the　performance　degradation　caused　by　interlayer　reflections　in　D2NNs　constructed　with　high-index　diffractive　masks.　In　classification　tasks,　T-D2NNs　achieve　better　classification　results　compared　to　D2NNs　that　consider　interlayer　reflections.