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Reliability issues stemming from device level non-idealities of non-volatile emerging technologies like ferroelectric field-effect transistors (FeFET), especially at scaled dimensions, cause substantial degradation in the accuracy of In-Memory crossbar-based AI systems. In this work, we present a variation-aware design technique to characterize the device level variations and to mitigate their impact on hardware accuracy employing a Bayesian Neural Network (BNN) approach. An effective conductance variation model is derived from the experimental measurements of cycle-to-cycle (C2C) and device-to-device (D2D) variations performed on FeFET devices fabricated using 28 nm high-$k$ metal gate technology. The variations were found to be a function of different conductance states within the given programming range, which sharply contrasts earlier efforts where a fixed variation dispersion was considered for all conductance values. Such variation characteristics formulated for three different device sizes at different read voltages were provided as prior variation information to the BNN to yield a more exact and reliable inference. Near-ideal accuracy for shallow networks (MLP5 and LeNet models) on the MNIST dataset and limited accuracy decline by $\sim$3.8-16.1% for deeper AlexNet models on CIFAR10 dataset under a wide range of variations corresponding to different device sizes and read voltages, demonstrates the efficacy of our proposed device-algorithm co-design technique.

Graphene is a promising material for applications as a channel in graphene field-effect transistors (GFETs) which may be used as a building block for optoelectronics, high-frequency devices and sensors. However, these devices require gate insulators which ideally should form atomically flat interfaces with graphene and at the same time contain small densities of traps to maintain high device stability. Previously used amorphous oxides, such as SiO2 and Al2O3, however, typically suffer from oxide dangling bonds at the interface, high surface roughness and numerous border oxide traps. In order to address these challenges, here we use for the first time 2nm thick epitaxial CaF2 as a gate insulator in GFETs. By analyzing device-to-device variability for over 200 devices fabricated in two batches, we find that tens of them show similar gate transfer characteristics. Our statistical analysis of the hysteresis up to 175C has revealed that while an ambient-sensitive counterclockwise hysteresis can be present in some devices, the dominant mechanism is thermally activated charge trapping by border defects in CaF2 which results in the conventional clockwise hysteresis. We demonstrate that both the hysteresis and bias-temperature instabilities in our GFETs with CaF2 are comparable to similar devices with SiO2 and Al2O3. In particular, we achieve a small hysteresis below 0.01 V for equivalent oxide thickness (EOT) of about 1 nm at the electric fields up to 15 MV/cm and sweep times in the kilosecond range. Thus, our results demonstrate that crystalline CaF2 is a promising insulator for highly-stable GFETs.

With ever increasing research on magnetic nano systems it is shown to have great potential in the areas of magnetic storage, biosensing, magnetoresistive insulation etc. In the field of biosensing specifically Spin Valve sensors coupled with Magnetic Nanolabels is showing great promise due to noise immunity and energy efficiency [1]. In this paper we present the application of injection locked based Spin Torque Nano Oscillator (STNO) suitable for high resolution energy efficient labeled DNA Detection. The proposed STNO microarray consists of 20 such devices oscillating at different frequencies making it possible to multiplex all the signals using capacitive coupling. Frequency Division Multiplexing can be aided with Time division multiplexing to increase the device integration and decrease the readout time while maintaining the same efficiency in presence of constant input referred noise.