ML to the Rescue: Reliability Estimation from Self-Heating and Aging in Transistors all the Way up Processors

With increasingly confined 3D structures and newly-adopted mate-rials of higher thermal resistance, transistor self-heating has risen to a critical reliability threat in state-of-the-art and emerging process nodes. One of the challenges of transistor self-heating is acceler-ated transistor aging, which leads to earlier failure of the chip if not considered appropriately. Nevertheless, adequate consideration of accelerated aging effects, induced by self-heating, throughout a large circuit design is profoundly challenging due to the large gap between where self-heating does originate (i.e., at the transis-tor level) and where its ultimate effect occurs (i.e., at the circuit and system levels). In this work, we demonstrate an end-to-end workflow starting from self-heating and aging effects in individual transistors all the way up to large circuits and processor designs. We demonstrate that with our accurately estimated degradations, the required timing guardband to ensure reliable operation of circuits is considerably reduced by up to 96 % compared to otherwise worst-case estimations that are conventionally employed.