Workload Compression Techniques to Scale Defect-Centric BTI Models to the Circuit Level

Bias Temperature Instability (BTI) poses a significant challenge in ensuring the reliability of digital systems, affecting the delay of digital logic gates, which ultimately can lead into timing failures. Sophisticated defect-centric models have been developed and successfully calibrated against empirical data to forecast the impacts of BTI at the device level. However, their application to large-scale digital circuits operating under realistic workloads over typical system lifetimes is limited because of the computational complexity of defect-centric models. To make the application of aging models in that context feasible, a useful technique is to compress the transistor workloads into simplified and hence manageable representative workloads. While fast in terms of execution speed, previous techniques struggle with accuracy when predicting aging degradation, and can reach a very high average error in threshold voltage increase prediction. In this work, we review the compression techniques described in the literature and propose two novel approaches that surpass existing ones in terms of accuracy, which is demonstrated for a complex digital design used as benchmark. Specifically, our best compression technique matches the predictions obtained through the reference uncompressed workloads, introducing negligible error, and maintains low execution times to efficiently and accurately scale defect-centric models to the circuit level.