Publication | Closed Access
In-Memory Computing Primitive for Sensor Data Fusion in 28 nm HKMG FeFET Technology
55
Citations
0
References
2018
Year
Unknown Venue
Non-volatile MemoryEngineeringSensor Data FusionComputer ArchitecturePhase Change MemoryIn-memory Computing PrimitiveMagnetoresistanceMagnetismMultiferroicsFerroelectric ApplicationNanoelectronicsInstrumentationHkmg Fefet TechnologyElectrical EngineeringPhysicsComputer EngineeringSpatio-temporal Switching DynamicsMicroelectronicsDomain PolarizationCondensed Matter PhysicsApplied PhysicsFerroelectric MaterialsSemiconductor Memory
In this work, we exploit the spatio-temporal switching dynamics of ferroelectric polarization to realize an energy-efficient, and massively-parallel in-memory computational primitive for at-node sensor data fusion and analytics based on an industrial 28nm HKMG FeFET technology [1]. We demonstrate: (i) the spatio-temporal dynamics of polarization switching in HfO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -based ferroelectrics under the stimuli of sub-coercive voltage pulses using experiments and phase-field modeling; (ii) an inherent rectifying conductance accumulation characteristic in FeFET with a large dynamic range of in the case of 3.0V, 50ns gate pulses; (iii) transition to more abrupt accumulation characteristics due to single/few domain polarization switching in scaled FeFET (34nm L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</sub> ); and (iv) successful detection of physiological anomalies from realworld multi-modal sensor data streams.