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Neuromorphic computing with non-volatile memory (NVM) can significantly improve performance and lower energy consumption of machine learning tasks implemented using spike-based computations and bio-inspired learning algorithms. High…
We have measured the transition process from the high to low resistivity states, i.e., the reset process of resistive switching based memristors based on Ni/HfO2/Si-n+ structures, and have also developed an analytical model for their…
The key feature of a memristor is that the resistance is a function of its previous resistance, thereby the behaviour of the device is influenced by changing the way in which potential is applied across it. Ultimately, information can be…
The instabilities arising in a one-dimensional beam sustained by the diffusive photorefractive nonlinearity in out-of-equilibrium ferroelectrics are theoretically and numerically investigated. In the "scale-free model", in striking contrast…
Reinforcement learning (RL) has demonstrated the ability to maintain the plasticity of the policy throughout short-term training in aerial robot control. However, these policies have been shown to loss of plasticity when extended to…
Data movement costs constitute a significant bottleneck in modern machine learning (ML) systems. When combined with the computational complexity of algorithms, such as neural networks, designing hardware accelerators with low energy…
As conventional memory technologies are challenged by their technological physical limits, emerging technologies driven by novel materials are becoming an attractive option for future memory architectures. Among these technologies,…
The dynamical conductivity of interacting multiband electronic systems derived in Ref.[1] is shown to be consistent with the general form of the Ward identity. Using the semiphenomenological form of this conductivity formula, we have…
Oscillatory phase pattern formation and amplitude control for a linearized stochastic neuron field model was investigated by simulating coupled stochastic processes defined by stochastic differential equations. It was found, for several…
Atomic Force Microscopy (AFM) combined with electrical modes provides a powerful contactless approach to characterize material electrical properties at the nanoscale. However, conventional electrostatic models often overlook dynamic charge…
Physical Unclonable Functions (PUFs) based on Non-Volatile Memory (NVM) technology have emerged as a promising solution for secure authentication and cryptographic applications. By leveraging the multi-level cell (MLC) characteristic of…
Multiphase systems are ubiquitous in engineering, biology, and materials science, where understanding their complex interactions and rheological behavior is crucial for advancing applications ranging from emulsion stability to cellular…
A multiphysics phase field model is used for the computational study of memristive thin film morphology and current-voltage hysteresis. In contrast to previous computational methods, no requirements are made on conducting filament geometry.…
Analog in-memory computing (AIMC) accelerators enable efficient deep neural network computation directly within memory using resistive crossbar arrays, where model parameters are represented by the conductance states of memristive devices.…
The dynamics of filaments in flow are central to understanding a wide range of biological and soft-matter systems, yet their behavior under time-dependent forcing remains poorly understood. Here, we investigate the long-time dynamics of…
This dissertation rigorously characterizes many modern commodity DRAM devices and shows that by exploiting DRAM access timing margins within manufacturer-recommended DRAM timing specifications, we can significantly improve system…
A parameter space procedure for designing chosen parameters of a repetitive controller to satisfy a robust performance criterion is presented. Using this method, low order robust repetitive controllers can be designed and implemented for…
A limited remanent polarization (Pr) in HfO2-based FeRAM remains a key obstacle to density scaling and reliability, while material and process optimizations offer only incremental improvements. This limitation fundamentally originates from…
Random media introduce large degrees of freedom in device design and can thus address challenges in manipulating optical waves. Wave shaping with metasurfaces has mainly utilized periodic or quasi-periodic grids, and, the potential of…
An extendable, efficient and explainable Machine Learning approach is proposed to represent cyclic plasticity and replace conventional material models based on the Radial Return Mapping algorithm. High accuracy and stability by means of a…