A synergistic approach for optimizing devices, circuits, and neural network architectures was used to abate junction-temperature-change-induced performance degradation of an Fe-FinFET-based artificial neural network. We demonstrated that the digital nature of the binarized neural network, with the “0” state programmed deep in the subthreshold and the “1” state in strong inversion, is crucial for robust deep neural network inference. The performance of a purely software-based binary neural network (BNN), with 96.1% accuracy for Modified National Institute of Standards and Technology (MNIST) handwritten digit recognition, was used as a baseline. The Fe-FinFET-based BNN (including device-to-device variation at 300 K) achieved 95.7% inference accuracy on the MNIST dataset. Although substantial inference accuracy degradation with temperature change was observed in a nonbinary neural network, the BNN with optimized Fe-FinFETs as synaptic devices had excellent resistance to temperature change effects, and maintained a minimum inference accuracy of 95.2% within a temperature range of −40 to 125 °C after gate stack and bias optimization. However, reprogramming to adjust device conductance was necessary for temperatures higher than 125 °C.

中文翻译：

---

通过门堆栈和铁电 FinFET 突触的偏置优化，实现从 233 K 到 398 K 的稳健二元神经网络操作


采用优化器件、电路和神经网络架构的协同方法来减轻结温变化引起的基于 Fe-FinFET 的人工神经网络的性能下降。我们证明了二值化神经网络的数字性质，即在亚阈值深处编程的“0”状态和强反转中的“1”状态，对于稳健的深度神经网络推理至关重要。纯基于软件的二进制神经网络 (BNN) 的性能被用作基准，该网络的改进国家标准与技术研究所 (MNIST) 手写数字识别准确率为 96.1%。基于 Fe-FinFET 的 BNN（包括 300 K 下的器件间变化）在 MNIST 数据集上实现了 95.7% 的推理精度。尽管在非二元神经网络中观察到推理精度随温度变化而大幅下降，但采用优化的 Fe-FinFET 作为突触器件的 BNN 具有出色的抗温度变化影响能力，在 -40 的温度范围内保持了 95.2% 的最低推理精度。栅极堆叠和偏置优化后达到 125 °C。然而，当温度高于 125 °C 时，需要重新编程来调整器件电导。