Quickly　predicting　the　remaining　useful　life　of　bipolar　transistors　is　an　effective　means　of　assessing　their　health　and　reliability.　Aiming　at　the　problems　of　high　modeling　difficulty　and　poor　fitting　accuracy　in　the　traditional　physical　model　method　and　data　fitting　method,　this　paper　proposes　a　reliability　life　prediction　method　for　bipolar　transistors　based　on　deep　learning.　Firstly,　the　experiment　is　carried　out　with　the　accelerated　storage　experimental　data　of　transistors　in　the　Microelectronics　Reliability　Laboratory　of　Beijing　University　of　Technology,　and　the　reverse　leakage　current　ICBO　is　selected　as　the　failure　sensitive　parameter,　and　a　reasonable　failure　judgment　is　set.　The　lifetime　of　the　sample　is　calculated;　Secondly,　the　average　lifetime　under　three　common　distributions　is　given,　and　combined　with　the　Peak　temperature　and　humidity　model,　the　storage　lifetime　of　the　transistor　under　natural　storage　conditions　is　extrapolated;　finally,　the　mean　absolute　error　(MAE)　and　mean　square　error　(MSE)　are　selected　as　evaluation　functions,　and　the　life　prediction　results　of　the　deep　learning　model　and　the　data　fitting　method　are　compared.　The　results　show　that　compared　with　the　data　fitting　method,　the　MAE　of　the　life　prediction　results　of　the　deep　learning　model　decreases　by　82.00%,　and　the　MSE　decreases　by　98.16%,　which　verifies　the　effectiveness　of　the　method.　©　COPYRIGHT　SPIE.　Downloading　of　the　abstract　is　permitted　for　personal　use　only.