9 research outputs found
Increasing the contrast of mammograms containing breast cancer regions on the background of fat involution using wavelet transformations
ΠΡΠ±ΠΎΡ ΡΡ Π΅ΠΌΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΌΠ΅ΠΌΡΠΈΡΡΠΎΡΠ½ΡΡ ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ²
Introduction. An array of memristive elements can be used in prospective neural computing systems as a programmable resistance (analog multiplication factor) when performing operations of analog vector multiplication, discrete in time. To form the required resistance, the memristor should be subjected to a programming procedure. This article discusses conventional programming schemes and proposes a new versatile programming scheme for memristor elements.Aim. To identify or develop an optimal programming scheme for memristors by analyzing the advantages and disadvantages of existing methods.Materials and methods. The programming procedure can be carried out using either SET or RESET, depending on a different direction of movement according to the volt-ampere characteristic of the memory and its transfer to a particular state. The programming process is controlled in the LTspice circuit modeling program.Results. Typical programming schemes of memristors were analyzed; advantages and disadvantages of existing methods were revealed. A new versatile circuit based on a variable resistor was proposed. The circuit was simulated both under a fixed resistance of the variable resistor and when varying the memristor resistance values within their permissible range.Conclusion. In comparison with the RESET mode, the SET programming mode provides for a greater linearity of variations in the memristor resistance. The use of a circuit based on a variable resistor and a bipolar voltage source allows programming of any type and eliminates the need for recommutation of the memristor. The simulation results confirm the feasibility of the proposed method. The proposed circuit can be complemented not only with a comparator, but also with an ADC. This will provide the possibility of selecting various means for measuring the memristor resistance both during programming and for the purpose of monitoring the memristor resistance at the end of the procedure.ΠΠ²Π΅Π΄Π΅Π½ΠΈΠ΅. ΠΠ°ΡΡΠΈΠ² ΠΌΠ΅ΠΌΡΠΈΡΡΠΈΠ²Π½ΡΡ
ΡΠ»Π΅ΠΌΠ΅Π½ΡΠΎΠ² ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ Π² ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
Π½Π΅ΠΉΡΠΎΠ²ΡΡΠΈΡΠ»Π΅Π½ΠΈΠΉ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΡΠ΅ΠΌΠΎΠ³ΠΎ ΡΠΎΠΏΡΠΎΡΠΈΠ²Π»Π΅Π½ΠΈΡ (Π°Π½Π°Π»ΠΎΠ³ΠΎΠ²ΠΎΠ³ΠΎ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠ° ΡΠΌΠ½ΠΎΠΆΠ΅Π½ΠΈΡ) ΠΏΡΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠΈ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ Π°Π½Π°Π»ΠΎΠ³ΠΎΠ²ΠΎΠ³ΠΎ ΡΠΌΠ½ΠΎΠΆΠ΅Π½ΠΈΡ Π²Π΅ΠΊΡΠΎΡΠΎΠ² Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎΠ³ΠΎ ΠΏΠΎ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ. ΠΠ»Ρ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΡΠ΅Π±ΡΠ΅ΠΌΠΎΠ³ΠΎ ΡΠΎΠΏΡΠΎΡΠΈΠ²Π»Π΅Π½ΠΈΡ ΠΌΠ΅ΠΌΡΠΈΡΡΠΎΡ Π΄ΠΎΠ»ΠΆΠ΅Π½ Π±ΡΡΡ ΠΏΠΎΠ΄Π²Π΅ΡΠ³Π½ΡΡ ΠΏΡΠΎΡΠ΅Π΄ΡΡΠ΅ "ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ". Π ΡΡΠ°ΡΡΠ΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ ΡΠΈΠΏΠΎΠ²ΡΠ΅ ΡΡ
Π΅ΠΌΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ Π½ΠΎΠ²Π°Ρ ΡΡ
Π΅ΠΌΠ° ΡΠ½ΠΈΠ²Π΅ΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΡΡΡΠΎΠΉΡΡΠ²Π° ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΌΠ΅ΠΌΡΠΈΡΡΠΎΡΠ°.Π¦Π΅Π»Ρ ΡΠ°Π±ΠΎΡΡ. ΠΡΡΠ²ΠΈΡΡ ΠΈΠ»ΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°ΡΡ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΡ ΡΡ
Π΅ΠΌΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΌΠ΅ΠΌΡΠΈΡΡΠΎΡΠΎΠ², Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΡ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π° ΠΈ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΊΠΈ ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΡ
ΡΠΏΠΎΡΠΎΠ±ΠΎΠ².ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΡΠΎΡΠ΅Π΄ΡΡΠ° ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΎΡΡΡΠ΅ΡΡΠ²Π»Π΅Π½Π° Π΄Π²ΡΠΌΡ ΡΠΏΠΎΡΠΎΠ±Π°ΠΌΠΈ β SET ΠΈ RESET, ΡΠ²ΡΠ·Π°Π½Π½ΡΠΌΠΈ Ρ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠΌ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΠΌ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠΎ Π²ΠΎΠ»ΡΡ-Π°ΠΌΠΏΠ΅ΡΠ½ΠΎΠΉ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ΅ ΠΌΠ΅ΠΌΡΠΈΡΡΠΎΡΠ° ΠΈ Π΅Π³ΠΎ ΠΏΠ΅ΡΠ΅Π²ΠΎΠ΄ΠΎΠΌ Π² ΡΠΎ ΠΈΠ»ΠΈ ΠΈΠ½ΠΎΠ΅ ΡΠΎΡΡΠΎΡΠ½ΠΈΠ΅. ΠΠΎΠ½ΡΡΠΎΠ»Ρ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ΅ΡΡΡ Π² ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ΅ ΡΡ
Π΅ΠΌΠΎΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ LTspice.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Ρ ΡΠΈΠΏΠΎΠ²ΡΠ΅ ΡΡ
Π΅ΠΌΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΌΠ΅ΠΌΡΠΈΡΡΠΎΡΠ°, Π²ΡΡΠ²Π»Π΅Π½Ρ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π° ΠΈ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΊΠΈ ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΡ
ΡΠΏΠΎΡΠΎΠ±ΠΎΠ². ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° Π½ΠΎΠ²Π°Ρ ΡΠ½ΠΈΠ²Π΅ΡΡΠ°Π»ΡΠ½Π°Ρ ΡΡ
Π΅ΠΌΠ° Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΡΠ΅Π·ΠΈΡΡΠΎΡΠ°. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ ΡΡ
Π΅ΠΌΠΎΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠΈ ΡΠΈΠΊΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΌ Π·Π½Π°ΡΠ΅Π½ΠΈΠΈ ΡΠΎΠΏΡΠΎΡΠΈΠ²Π»Π΅Π½ΠΈΡ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΡΠ΅Π·ΠΈΡΡΠΎΡΠ° ΠΈ ΠΏΡΠΈ Π²Π°ΡΠΈΠ°ΡΠΈΠΈ ΡΠ°Π·Π½ΡΡ
Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΡΠΎΠΏΡΠΎΡΠΈΠ²Π»Π΅Π½ΠΈΡ Π² ΠΏΡΠ΅Π΄Π΅Π»Π°Ρ
Π΄ΠΎΠΏΡΡΡΠΈΠΌΡΡ
Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΡΠΎΠΏΡΠΎΡΠΈΠ²Π»Π΅Π½ΠΈΠΉ ΠΌΠ΅ΠΌΡΠΈΡΡΠΎΡΠ°.ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. Π Π΅ΠΆΠΈΠΌ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ SET ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ Π΄ΠΎΡΡΠΈΡΡ Π±ΠΎΠ»ΡΡΠ΅ΠΉ Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΡΡΠΈ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠΎΠΏΡΠΎΡΠΈΠ²Π»Π΅Π½ΠΈΡ ΠΌΠ΅ΠΌΡΠΈΡΡΠΎΡΠ° ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ ΡΠ΅ΠΆΠΈΠΌΠΎΠΌ RESET. ΠΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΡΡ
Π΅ΠΌΡ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΡΠ΅Π·ΠΈΡΡΠΎΡΠ° ΠΈ Π΄Π²ΡΡ
ΠΏΠΎΠ»ΡΡΠ½ΠΎΠ³ΠΎ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ° Π½Π°ΠΏΡΡΠΆΠ΅Π½ΠΈΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΎΡΡΡΠ΅ΡΡΠ²ΠΈΡΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π»ΡΠ±ΠΎΠ³ΠΎ ΡΠΈΠΏΠ° ΠΈ ΠΈΡΠΊΠ»ΡΡΠ°Π΅Ρ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ ΠΏΠ΅ΡΠ΅ΠΊΠΎΠΌΠΌΡΡΠ°ΡΠΈΠΈ ΠΌΠ΅ΠΌΡΠΈΡΡΠΎΡΠ°. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°ΡΡ ΡΠ°Π±ΠΎΡΠΎΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠ³ΠΎ ΡΠΏΠΎΡΠΎΠ±Π°. ΠΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΊ Π½Π°Π»ΠΈΡΠΈΡ ΠΊΠΎΠΌΠΏΠ°ΡΠ°ΡΠΎΡΠ° Π² ΡΡ
Π΅ΠΌΡ ΠΌΠΎΠΆΠ½ΠΎ Π²Π²Π΅ΡΡΠΈ ΠΈ ΠΠ¦Π Π΄Π»Ρ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ Π²ΡΠ±ΠΎΡΠ° ΡΡΠ΅Π΄ΡΡΠ²Π° ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΡΠΎΠΏΡΠΎΡΠΈΠ²Π»Π΅Π½ΠΈΡ ΠΌΠ΅ΠΌΡΠΈΡΡΠΎΡΠ° ΠΊΠ°ΠΊ Π² ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΡΠ°ΠΊ ΠΈ Π΄Π»Ρ ΡΠ΅Π»Π΅ΠΉ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ ΡΠΎΠΏΡΠΎΡΠΈΠ²Π»Π΅Π½ΠΈΡ ΠΌΠ΅ΠΌΡΠΈΡΡΠΎΡΠ° ΠΏΠΎ ΠΎΠΊΠΎΠ½ΡΠ°Π½ΠΈΠΈ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ
The influence of algorithms for tuning the parameters of neuromorphic systems on their fault tolerance
Effect of pulse amplitude on depression and potentiation of ZrO2(Y)-based memristive synaptic device
The effect of the amplitude of depressing and potentiating pulses on the synaptic plasticity (potentiation and depression) of a memristive device based on the Ta/ZrO2(Y)/Pt stack has been experimentally studied. It is shown that the amplitude of depressing and potentiating pulses affects the synaptic plasticity, namely, the value of current passing through the memristive device. The presented results demonstrate the prospects of using the ZrO2(Y)-based memristive devices as stable and low-power elements of neuromorphic systems
Neurohybrid memristive cmos-integrated systems for biosensors and neuroprosthetics
Β© 2020 Mikhaylov, Pimashkin, Pigareva, Gerasimova, Gryaznov, Shchanikov, Zuev, Talanov, Lavrov, Demin, Erokhin, Lobov, Mukhina, Kazantsev, Wu and Spagnolo. Here we provide a perspective concept of neurohybrid memristive chip based on the combination of living neural networks cultivated in microfluidic/microelectrode system, metal-oxide memristive devices or arrays integrated with mixed-signal CMOS layer to control the analog memristive circuits, process the decoded information, and arrange a feedback stimulation of biological culture as parts of a bidirectional neurointerface. Our main focus is on the state-of-the-art approaches for cultivation and spatial ordering of the network of dissociated hippocampal neuron cells, fabrication of a large-scale cross-bar array of memristive devices tailored using device engineering, resistive state programming, or non-linear dynamics, as well as hardware implementation of spiking neural networks (SNNs) based on the arrays of memristive devices and integrated CMOS electronics. The concept represents an example of a brain-on-chip system belonging to a more general class of memristive neurohybrid systems for a newgeneration robotics, artificial intelligence, and personalized medicine, discussed in the framework of the proposed roadmap for the next decade period
Using simulation to define the tolerances for the information and physical parameters of memristors-based artificial neural networks
Quantitative determination of fault tolerance of memristor-based artificial neural networks
Silicon-Compatible Memristive Devices Tailored by Laser and Thermal Treatments
Nowadays, memristors are of considerable interest to researchers and engineers due to the promise they hold for the creation of power-efficient memristor-based information or computing systems. In particular, this refers to memristive devices based on the resistive switching phenomenon, which in most cases are fabricated in the form of metal–insulator–metal structures. At the same time, the demand for compatibility with the standard fabrication process of complementary metal–oxide semiconductors makes it relevant from a practical point of view to fabricate memristive devices directly on a silicon or SOI (silicon on insulator) substrate. Here we have investigated the electrical characteristics and resistive switching of SiOx- and SiNx-based memristors fabricated on SOI substrates and subjected to additional laser treatment and thermal treatment. The investigated memristors do not require electroforming and demonstrate a synaptic type of resistive switching. It is found that the parameters of resistive switching of SiOx- and SiNx-based memristors on SOI substrates are remarkably improved. In particular, the laser treatment gives rise to a significant increase in the hysteresis loop in I–V curves of SiNx-based memristors. Moreover, for SiOx-based memristors, the thermal treatment used after the laser treatment produces a notable decrease in the resistive switching voltage
Noise-assisted persistence and recovery of memory state in a memristive spiking neuromorphic network
We investigate the constructive role of an external noise signal, in the form of a low-rate Poisson sequence of pulses supplied to all inputs of a spiking neural network, consisting in maintaining for a long time or even recovering a memory trace (engram) of the image without its direct renewal (or rewriting). In particular, this unique dynamic property is demonstrated in a single-layer spiking neural network consisting of simple integrate-and-fire neurons and memristive synaptic weights. This is carried out by preserving and even fine-tuning the conductance values of memristors in terms of dynamic plasticity, specifically spike-timing-dependent plasticity-type, driven by overlapping pre- and postsynaptic voltage spikes. It has been shown that the weights can be to a certain extent unreliable, due to such characteristics as the limited retention time of resistive state or the variation of switching voltages. Such a noise-assisted persistence of memory, on one hand, could be a prototypical mechanism in a biological nervous system and, on the other hand, brings one step closer to the possibility of building reliable spiking neural networks composed of unreliable analog elements