Characterization of Drain Current Variations in FeFETs for PIM-based DNN Accelerators

We analyze the impact of drain current (IDS) variation in 28 nm high-K metal-gate Ferroelectric FET devices on FeFET-based processing-in-memory (PIM) deep neural network (DNN) accelerators. Non-Normal variation in IDS is observed due to repeated read operation on FeFET devices with different channel dimensions at various read frequencies. Device-circuit co-analysis using the measured current distribution shows a 1 to 3 percent accuracy degradation of an FeFET-based PIM platform when classifying the Fashion-MNIST dataset with the LeNET-5 DNN model. This accuracy drop can be fully recovered with variation-aware training methods, showing that individual FeFET device current variation over many read cycles is not prohibitive to the design of DNN accelerators.