This article reports a novel ferroelectric fieldeffect transistor (FeFET)-based crossbar array cascaded with an external resistor. The external resistor is shunted with the column of the FeFET array, as a current limiter and reduces the impact of variations in drain current (I d ), especially in a low threshold voltage (LVT) state. We have designed crossbar arrays of 8 × 8 sizes and performed multiply-and-accumulate (MAC) operations. Furthermore, we have evaluated the performance of the current limited FeFET crossbar array in system-level applications. Finally, the system-level performance evaluation was done by neuromorphic simulation of the resistor-shunted FeFET crossbar array. The crossbar array achieved software-comparable inference accuracy (∼97%) for National Institute of Standards and Technology (MNIST) datasets with multilayer perceptron (MLP) neural network, whereas the crossbar arrays built solely with FeFETs failed to learn, yielding only 9.8% accuracy.
Recent advances in artificial intelligence (AI) have led to successful solutions for numerous applications by utilizing deep neural network (DNN) architectures. [1] Hence, specialized hardware accelerators have been developed to facilitate highspeed computations for these data-intensive workloads. [2] While these computational engines have led to several advanced applications at the cloud-scale, true benefits of AI can be realized by enabling low-power edge computing. For Internet of Things (IoT) devices with constrained area and power, performing highprecision computations becomes infeasible. Quantization of neural network (NN) weights and activations has been explored as a means to reduce the energy cost of computations while preserving computational accuracy. [3] However, memory capacity and bandwidth can be observed as primary limiting factors
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