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This paper investigates and compares through a comprehensive TCAD analysis 2D and 3D simulations for ferroelectric based FETs. We provide clear evidence that the multiple read conductance values experimentally observed in FeFETs stem from…
Forecasting chaotic time series requires models that can capture the intrinsic geometry of the underlying attractor while remaining computationally efficient. We introduce a novel reservoir computing (RC) framework that integrates…
Nonlinear spin dynamics in magnetic materials offers a promising avenue for implementing physical reservoir computing, one of the most accomplished brain-inspired frameworks for information processing. In this study, we investigate the…
As the conventional scaling of logic devices comes to an end, functional wafer backside and 3D transistor stacking are consensus for next-generation logic technology, offering considerable design space extension for powers, signals or even…
Fourier solvers have become efficient tools to establish structure-property relations in heterogeneous materials. Introduced as an alternative to the Finite Element (FE) method, they are based on fixed-point solutions of the…
Ferroelectric non-volatile capacitance-based memories enable non-destructive readout and low-power in-memory computing with 3D stacking potential. However, their limited memory window (1-10 fF/{\mu}m) requires material-device-circuit…
Reservoir computing is a powerful tool for forecasting turbulence because its simple architecture has the computational efficiency to handle large systems. Its implementation, however, often requires full state-vector measurements and…
Recent advances in quantum computing have demonstrated its potential to significantly enhance the analysis and forecasting of complex classical data. Among these, quantum reservoir computing has emerged as a particularly powerful approach,…
Quantum reservoir computing is a neuro-inspired machine learning approach harnessing the rich dynamics of quantum systems to solve temporal tasks. It has gathered attention for its suitability for NISQ devices, for easy and fast…
Electrical measurements of ferroelectric switching kinetics are widely used to probe the dynamics of polarization reversal, yet the influence of the measurement circuit is often underappreciated. In this paper, we show that the interplay…
Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor-based circuits are particularly promising for RC, as…
Making accurate predictions of chaotic time series is a complex challenge. Reservoir computing, a neuromorphic-inspired approach, has emerged as a powerful tool for this task. It exploits the memory and nonlinearity of dynamical systems…
Ferroelectric polarization switching, achieved by mechanical forces, enables the storage of stress information in ferroelectrics, and holds promise for human-interfacing applications. The prevailing mechanical approach is locally induced…
A reservoir computer is a way of using a high dimensional dynamical system for computation. One way to construct a reservoir computer is by connecting a set of nonlinear nodes into a network. Because the network creates feedback between…
Ferroelectric field-effect transistors (FeFET) with two-dimensional (2D) semiconductor channels are promising low-power, embedded non-volatile memory (NVM) candidates for next-generation in-memory computing. However, the performance of…
Ferroelectric devices use their electric polarization ferroic order as the switching and storage physical quantity for memory applications. However, additional built-in physical quantities and memory paradigms are requested for…
Physical reservoir computing, which is a promising method for the implementation of highly efficient artificial intelligence devices, requires a physical system with nonlinearity, fading memory, and the ability to map in high dimensions.…
Due to rising electricity demand, accurate short-term load forecasting is increasingly important for grid stability and efficient energy management, particularly in resource-constrained edge settings. We present a hardware-efficient Quantum…
A probabilistic-bit (p-bit) is the fundamental building block in the circuit network of a stochastic computing, and it could produce a continuous random bit-stream with tunable probability. Utilizing the stochasticity in few-domain…
Topologically protected edge state transport in quantum materials is dissipationless and features quantized Hall conductance, and shows great potential in highly fault-tolerant computing technologies. However, it remains elusive about how…