This　paper　presents　a　BJT-based　smart　CMOS　temperature　sensor.　The　analog　front-end　circuit　contains　a　bias　circuit　and　a　bipolar　core;　the　data　conversion　interface　features　an　incremental　delta-sigma　analog-to-digital　converter.　The　circuit　utilizes　the　chopping,　correlated　double　sampling,　and　dynamic　element　matching　techniques　to　mitigate　the　effects　of　process　bias　and　nonideal　device　characteristics　on　measurement　accuracy.　Furthermore,　based　on　the　principle　of　charge　conservation,　the　dynamic　range　utilization　of　the　ADC　increases.　We　propose　a　neural　network　that　uses　a　multilayer　convolutional　perceptron　to　calibrate　the　sensor　output　results.　Using　the　algorithm,　the　sensor　achieves　an　inaccuracy　of　±0.11　°C　(3σ),　exceeding　the　accuracy　of　±0.23　°C　(3σ)　achieved　without　calibration.　We　implement　the　sensor　in　a　0.18　µm　CMOS　process,　occupying　an　area　of　0.42　mm2.　It　achieves　a　resolution　of　0.01　°C　and　has　a　conversion　time　of　24　ms.　©　2023　by　the　authors.