Linear Synaptic Weight Update in Selector-Less HfO $_{\text{2}}$ RRAM Using Al $_{\text{2}}$ O $_{\text{3}}$ Built-In Resistor for Neuromorphic Computing Systems

The engineering filament evolution of HfO
$_{\text{2}}$
-based resistive random access memory (RRAM) has shown promising advancements in analog synaptic weight updates during the training stage for neuromorphic systems. However, the significance of incorporating an additional selector to eliminate sneak-path currents has often been neglected. Therefore, this study addresses this issue by demonstrating linearly and symmetrically tuned synaptic weights in selector-less HfO
$_{\text{2}}$
-based RRAM. By introducing an extremely thin Al
$_{\text{2}}$
O
$_{\text{3}}$
layer in the HfO
$_{\text{2}}$
RRAM, we observed a nonlinear current–voltage behavior that effectively suppresses low-resistance states in the low-voltage regime, which act as sneak-path currents. Through simple numerical fitting, we determined that the Al
$_{\text{2}}$
O
$_{\text{3}}$
layer functions as a built-in exponential resistor, and we investigated the impact of its thickness on the switching behavior. To further understand the role of the Al
$_{\text{2}}$
O
$_{\text{3}}$
layer, we analyzed the set-switching mechanism of the selector-less RRAM by examining the real-time transient current response. Unlike an abrupt current jump typically observed in conventional RRAMs, we observed a sequential transition in the selector-less RRAM. This implies that conduction in the selector-less RRAM is in two steps through each oxide of the Al
$_{\text{2}}$
O
$_{\text{3}}$
/HfO
$_{\text{2}}$
stack. Therefore, the utilization of the Al
$_{\text{2}}$
O
$_{\text{3}}$
both enables analogously modulated current response through an identical pulse scheme for the selected cell and suppresses unwanted updates of half-selected cells. The improved linearity of synaptic weight updates in the selector-less Al
$_{\text{2}}$
O
$_{\text{3}}$
/HfO
$_{\text{2}}$
RRAM allows for high pattern recognition accuracy on the MNIST dataset, based on the backpropagation algorithm performed in IBM AIHWKIT simulations.