The　die　shear　test　is　a　feasible　and　conventional　method　to　characterize　the　shear　strength　of　die-attaching　layer　materials　in　electronic　packaging.　A　new　method　for　determining　cohesive　zone　model　(CZM)　parameters　using　deep　neural　networks　(DNN)　and　die　shear　tests　is　proposed,　different　from　classical　fracture　framework　or　lap　shear　test-based　methods.　With　the　sintered　nano-silver　die　shear　test,　the　results　show　that　the　bilinear　CZM　inversion　results　agree　well　with　the　experimental　results.　It　is　found　that　the　DNN　model　has　high　accuracy　in　predicting　and　identifying　the　maximum　shear　traction　strength　tau　max,　separation　displacement　of　the　interface　delta　f,　and　the　interface　stiffness　k1　of　CZM　parameters　for　sintered　nano-silver　adhesive　layer　through　die　shear　test　load　versus　displacement　curves.　The　presented　DNN-aided　inverse　identifying　method　through　the　die　shear　test　in　this　paper　could　provide　an　alternative　and　convenient　method　for　extracting　CZM　parameters　of　various　kinds　of　adhesive　materials　in　electronic　packaging.　Die　shear　tests　were　used　for　the　inverse　identification　of　CZM　parameters.The　die　shear　test　P-delta　curves　were　established　as　the　dataset.A　DNN-aided　CZM　inverse　identification　method　was　proposed.The　DNN-aided　model　can　accurately　identify　the　CZM　parameters.