<p>This letter proposes a memory cell, denoted by 1F-1T, consisting of a ferroelectric field-effect transistor (Fe-FET) cascoded with another current-limiting transistor (T). The transistor reduces the impact of drain current (Id) variations by limiting the on-state current in FeFET, denoted by 1F. We have fabricated 28nm high-k-meta-gate (HKMG) based FeFETs, and the experimental data is used to model and simulate single-cell and memory arrays. The simulation shows significant improvement in bit-line (BL) current (IBL ) variation for 1F-1T memory array. Finally, the system-level neuromorphic simulation with 1F-1T synapses shows an inference accuracy of 97.6% for MNIST hand-written digits with multi-layer perceptron (MLP) neural networks.</p>

<p>This letter proposes a memory cell, denoted by 1F-1T, consisting of a ferroelectric field-effect transistor (Fe-FET) cascoded with another current-limiting transistor (T). The transistor reduces the impact of drain current (Id) variations by limiting the on-state current in FeFET, denoted by 1F. We have fabricated 28nm high-k-meta-gate (HKMG) based FeFETs, and the experimental data is used to model and simulate single-cell and memory arrays. The simulation shows significant improvement in bit-line (BL) current (IBL ) variation for 1F-1T memory array. Finally, the system-level neuromorphic simulation with 1F-1T synapses shows an inference accuracy of 97.6% for MNIST hand-written digits with multi-layer perceptron (MLP) neural networks.</p>

<p>This paper presents a comprehensive overview of 28 nm high-k-metal gate-based ferroelectric field effect transistor devices for synaptic applications. The device under test was fabricated on 300mm wafers at GlobalFoundries. The fabricated devices demonstrate 10³ WRITE-endurance cycles and 10⁴ seconds of data-retention capability at 85°C. We have also assessed the FeFET-based crossbar array’s performance in system-level applications. By simulating the FeFET crossbar array for neuromorphic applications, the system performance was assessed. For datasets from the National Institute of Standards and Technology (MNIST), the crossbar array achieved software-comparable inference accuracy of about 97% using multilayer perceptron (MLP) neural networks.</p>