Details

Autor(en) / Beteiligte

• Wei, Z.
• Kim, W.
• Wang, Z.
• Hu, L.
• Jung, D.
• Zhang, J.
• Huai, Y.

Titel

Accurate and Fast STT-MRAM Endurance Evaluation Using a Novel Metric for Asymmetric Bipolar Stress and Deep Learning

Ist Teil von

• 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits), 2022, p.373-374

Ort / Verlag

IEEE

Erscheinungsjahr

2022

Quelle

IEEE Xplore

Beschreibungen/Notizen

• A framework for STT-MRAM endurance test and prediction has been successfully developed. To improve prediction accuracy, we have constructed a Unified Voltage Polarities Power-law model (UVPP) using a novel stress metric by unifying unipolar, symmetric bipolar and asymmetric bipolar stresses. To keep TDDB test within a realistic time frame, we have applied a time series deep learning model, Transformer, for forecasting MTJ resistance series data of 10 6 -10 15 cycles and breakdown points by using tested resistance series data of 1-10 6 cycles, which reduces test time by up to 10 9 x. The proposed framework enables fast STT-MRAM reliability verification for high-volume mass production.