In-Memory Computing Primitive for Sensor Data Fusion in 28 nm HKMG FeFET Technology

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 HfO2-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 $G_{\max}/G_{\min} > 100$ 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 LG); and (iv) successful detection of physiological anomalies from realworld multi-modal sensor data streams.