A5042286591- Advanced Memory and Neural Computing
- Neuroscience and Neural Engineering
- Neural dynamics and brain function
- Ferroelectric and Negative Capacitance Devices
- Photoreceptor and optogenetics research
- Transition Metal Oxide Nanomaterials
- CCD and CMOS Imaging Sensors
- Conducting polymers and applications
- stochastic dynamics and bifurcation
- Surface Roughness and Optical Measurements
- Machine Learning in Materials Science
- Tactile and Sensory Interactions
- Advancements in Battery Materials
- Magnetic properties of thin films
- Phase-change materials and chalcogenides
- Nonlinear Optical Materials Studies
- Laser Material Processing Techniques
- Neural Networks and Reservoir Computing
- Magnetism in coordination complexes
- Magneto-Optical Properties and Applications
- Semiconductor materials and interfaces
- Graphene research and applications
Technische Universität Ilmenau
2023-2024
Kurchatov Institute
2018-2023
Moscow Institute of Physics and Technology
2019
In this paper, the resistive switching and neuromorphic behavior of memristive devices based on parylene, a polymer both low-cost safe for human body, is comprehensively studied. The Metal/Parylene/ITO sandwich structures were prepared by means standard gas phase surface polymerization method with different top active metal electrodes (Ag, Al, Cu or Ti about 500 nm thickness). These organic exhibit excellent performance: low voltage (down to 1 V), large OFF/ON resistance ratio (about 10^3),...
Resistive and capacitive switching in capacitor metal/nanocomposite/metal (M/NC/M) structures based on (CoFeB)x(LiNbO3)100-x NC fabricated by ion-beam sputtering with metal content x $\approx$ 8-20 at. % is studied. The peculiarity of the structure synthesis was use increased oxygen ($\approx$ 2*10^-5 Torr) at initial stage growth. films, along nanogranules 3-6 nm size, contained a large number dispersed Co (Fe) atoms (up to ~10^22 cm^-3). Measurements were performed both DC AC (frequency...
Neuromorphic systems consisting of artificial neurons and memristive synapses could provide a much better performance significantly more energy-efficient approach to the implementation different types neural network algorithms than traditional hardware with Von-Neumann architecture. However, weight adjustment in formal neuromorphic networks by standard back-propagation techniques suffers from poor device-to-device reproducibility. One most promising approaches overcome this problem is use...
The development of memristor-based spiking neuromorphic systems (NS) has been essentially driven by the hope to replicate extremely high energy efficiency biological systems. Spike-timing-dependent plasticity (STDP) mechanism is considered as one most promising learning rules for NS. STDP observed in different types synapses presence neuromodulators, e.g. dopamine, and believed be an enabling phenomenon important functions such associative reinforcement learning. However, direct window...
Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer critical dimensions, low power consumption, functional similarity to biological synapses. Most studies on memristor-based use either software models memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting fixed pre-trained explainable feature...
MLP-Mixer based on multilayer perceptrons (MLPs) is a novel architecture of neuromorphic computing system (NCS) introduced for image classification tasks without convolutional layers. Its software realization demonstrates high accuracy, although the number trainable weights relatively low. One more promising way improving NCS performance, especially in terms power consumption, its hardware using memristors. Therefore, this work, we proposed an with adapted and memristive weights. For...
Abstract Blooming and pruning is one of the most important developmental mechanisms biological brain in first years life, enabling it to adapt its network structure demands environment. The mechanism thought be fundamental for development cognitive skills. Inspired by this, Chialvo Bak proposed 1999 a learning scheme that learns from mistakes eliminating initial surplus synaptic connections those lead an undesirable outcome. Here, this idea implemented neuromorphic circuit using CMOS...
In hardware neuromorphic systems (NSs), memristors are used as synaptic connections. such systems, spike‐timing‐dependent plasticity (STDP) is a promising local learning rule. Herein, STDP studied in system composed of pair or software neurons connected by (CoFeB) x (LiNbO 3 ) 100− nanocomposite‐based memristor. The dopamine‐like modulation memristor‐based implemented simply the change polarity spikes generated artificial operating inhibitory excitatory mode. This method shown to be...
Using (Co <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">40</sub> Fe B xmlns:xlink="http://www.w3.org/1999/xlink">20</sub> ) xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> (LiNbO xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> xmlns:xlink="http://www.w3.org/1999/xlink">100-x</sub> (x = 6-55 at.%) nanocomposite films as an example, we have performed comparative investigations of granular systems properties with a high (~10 <sup...