Ferroelectric Tunnel Junction Memristors for In-Memory Computing Accelerators

Neuromorphic computing has seen great interest as leaps in artificial intelligence (AI) applications have exposed limitations due to heavy memory access, with the von Neumann computing architecture. The parallel in-memory computing provided by neuromorphic computing has the potential to significantly improve latency and power consumption. Key to analog neuromorphic computing hardware are memristors, providing non-volatile multistate conductance levels, high switching speed, and energy efficiency. Ferroelectric tunnel junction (FTJ) memristors are prime candidates for this purpose, but the impact of the particular characteristics for their performance upon integration into large crossbar arrays, the core compute element for both inference and training in deep neural networks, requires close investigation. In this work, a W/Hf_xZr_1−xO₂/TiN FTJ with 60 programmable conductance states, a dynamic range (DR) up to 10, current density >3 A m⁻² at V _read = 0.3 V and highly nonlinear current–voltage (I–V) characteristics (>1100) is experimentally demonstrated. Using a circuit macro-model, the system level performance of a true crossbar array is evaluated and a 92% classification accuracy of the modified nation institute of science and technology (MNIST) dataset is achieved. Finally, the low on conductance in combination with the highly nonlinear I–V characteristics enable the realization of large selector-free crossbar arrays for neuromorphic hardware accelerators.