Recently,　hybrid-row-height　designs　have　been　introduced　to　achieve　performance　and　area　co-optimization　in　advanced　nodes.　Hybrid-row-height　designs　incur　challenging　issues　to　layout　due　to　the　heterogeneous　cell　and　row　structures.　In　this　paper,　we　present　an　effective　algorithm　to　address　the　hybrid-row-height　placement　problem　in　two　major　stages:　(1)　global　placement,　and　(2)　legalization.　Inspired　by　the　multi-channel　processing　method　in　convolutional　neural　networks　(CNN),　we　use　the　feature　extraction　technique　to　equivalently　transform　the　hybrid-row-height　global　placement　problem　into　two　sub-problems　that　can　be　solved　effectively.　We　propose　a　multi-layer　nonlinear　framework　with　alignment　guidance　and　a　self-adaptive　parameter　adjustment　scheme,　which　can　obtain　a　high-quality　solution　to　the　hybrid-row-height　global　placement　problem.　In　the　legalization　stage,　we　formulate　the　hybrid-row-height　legalization　problem　into　a　convex　quadratic　programming　(QP)　problem,　then　apply　the　robust　modulus-based　matrix　splitting　iteration　method　(RMMSIM)　to　solve　the　QP　efficiently.　After　RMMSIM-based　global　legalization,　Tetris-like　allocation　is　used　to　resolve　remaining　physical　violations.　Compared　with　the　state-of-the-art　work,　experiments　on　the　2015　ISPD　Contest　benchmarks　show　that　our　algorithm　can　achieve　7%　shorter　final　total　wirelength　and　2.23x　speedup.