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Improved State Stability of HfO2 Ferroelectric Tunnel Junction by Template-Induced Crystallization and Remote Scavenging for Efficient in-Memory Reinforcement Learning
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2020
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EngineeringEmerging Memory TechnologyFerroelectric Random-access MemoryIn-memory Reinforcement LearningPhase Change MemoryFerroelectric ApplicationQuantum MaterialsMaterials ScienceRead Current InstabilitiesElectrical EngineeringDepolarization FieldElectronic MemoryTemplate-induced CrystallizationFerroelasticsElectronic MaterialsApplied PhysicsCondensed Matter PhysicsFerroelectric MaterialsRemote ScavengingSemiconductor MemoryState Stability
We investigated the effects of read current instabilities originated from depolarization field and charge trapping in HfO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ferroelectric tunnel junctions (FTJs) on the performance of in-memory reinforcement learning. We utilized, for the first time, remote scavenging to control interfacial layer thickness, combined with template-induced crystallization to stabilize the ferroelectric phase. These are found to improve both short-term and long-term stability of memory state. Pole-cart simulation results reveal that these improvements significantly contribute to the efficiency and stability of reinforcement learning with the FTJ cross-point array.