Bonding　strength　of　sintered　nano　silver　(Ag)　joints　has　been　an　important　index　for　evaluating　the　reliability　of　power　module　packages,　which　has　been　reported　to　be　influenced　by　many　factors.　However,　it　is　hard　to　evaluate　and　predict　those　factors　affecting　bonding　strength.　With　the　help　of　artificial　intelligence　(AI),　the　utilization　of　AI　tools　to　assist　in　finding　science　solutions　has　become　a　mainstream　consensus.　In　this　paper,　we　will　show　how　to　evaluate　and　predict　those　sintered　nano　Ag-Al　bonded　joints　bonding　strength　with　deep　learning　(DL)　methods.　Firstly,　a　reliable　extended　dataset　was　obtained　using　the　Conditional　Tabular　Generative　Adversarial　Networks　(CTGAN)　based　on　the　dataset　of　sintered　Ag-Al　interface　shear　strength　with　four　different　metallization　layers　under　different　high-temperature　aging　time　with　die　shear　test.　Subsequently,　four　DL　models　were　adopted　to　predict　the　shear　strength　of　Ag-Al　interface　under　different　metallization　layers　to　evaluate　the　interface　bonding　strength,　all　with　high　level　of　determination　coefficient　(R2,　above　0.99)　and　classification　accuracy　(above　85%).　Last　but　not　least,　factors　influencing　the　shear　strength　of　Ag-Al　interface　were　analyzed　and　ranked　by　weight　analysis　and　SHapley　Additive　exPlanations　(SHAP)　method.　This　research　could　provide　a　novel　perspective　on　understanding　those　factors　affecting　the　shear　strength　of　sintered　nano　Ag　interconnect　layer　in　power　devices.　©　2024　Elsevier　Ltd