Unraveling the Dynamics of HfO $_\text{2}$ -Based NW-CTT as an Artificial Synapse

In this work, we investigate the synaptic characteristics of HfO
$_\text{2}$
-based nanowire-charge trap transistor (NW-CTT) using a fully calibrated 3-D technology computer-aided design (TCAD) simulation, based on self-consistent solutions of 3-D Poisson’s equation, Boltzmann transport equation, and self-heating equation with interface traps charge correction terms. The results indicate that the Si NW-CTT exhibits excellent nonvolatile memory characteristics with the modulation of threshold voltage around 1 V between program and erase pulses when interface trap charge density (
${\textit{N}_{\text{it}}}\text{)} = \text{1} \ttimes {\text{10}^{\text{17}}}~{\text{c}}{{\text{m}}^{{{ - \text{3}}}}}$
present in the HfO
$_\text{2}$
layer. In addition, almost linear conductance modulation in long-term potentiation (LTP) and long-term depression (LTD) with NW-CTT synapse promises the recognition accuracy of around 94.7% and write energy efficiency of around 2.3 mJ in the 784
$\times$
100
$\times$
10 neural network for handwritten MNIST dataset. Our analysis highlights the critical role of optimal nanowire diameter, thickness of the charge trapping HfO
$_\text{2}$
layer, gate length, and metal gate work function in enhancing short-and long-term memory characteristics, while concurrently preserving recognition accuracy and energy efficiency. Overall, our results provide valuable insights into the synaptic behavior of conventional NW-CTTs and offer guidance for further harnessing their weight-update capabilities in neuromorphic computing applications.