Release:2020. Vol. 6. № 3 (23)
About the authors:Alexander D. Pisarev, Cand. Sci. (Tech.), Associate Professor, Department of Applied and Technical Physics, Head of Laboratory of Beam-Plasma Technologies, SEC “Nanotechnologies”, University of Tyumen; eLibrary AuthorID, ORCID, Scopus AuthorID, email@example.com
The aim of this article lies in checking the efficiency of memory and logic matrices. Achieving this has required producing a composite memristor-diode crossbar and studying its electrophysical properties. For these purposes, the authors have made a measuring bench, which consists of a composite memristor-diode crossbar, control peripheral circuitry, based on discrete elements with CMOS logic, and Keithley SourceMeter 2400.
The silicon junction p-Si/n-Si has been chosen because its electrical properties better suit the Zenner diode’s requirements compared to the p-Si/ZnO junction. The memristor-diode crossbar with the TiN/Ti0,93Al0,07Ox/p-Si/n-Si/W structure was made with implementation of a new diode. The results show that the crossbar cell with a p-Si/n-Si diode has better rectifying properties in comparison with a p-Si/ZnOx diode, because the current in the crossbar cell with positive voltage bias is much higher than with negative voltage bias. Strong rectifying properties of the cell are necessary for the functioning of diode logic in the logic matrix and for memristor state recording in the logic and memory matrices.
The study of electrophysical properties of the composite memristor-diode crossbar, measurement of current-voltage characteristics of the diode and composite memristor-diode crossbar cell and signal processing were performed. The signal processing was performed in the following modes: addition of output impulses of neurons and their routing to synapses of other neurons; multiplication of number matrix by vector, performed in the memory matrix with weighing and totalling of signals; and associative self-learning.
For the first time, the generation of a new association (new knowledge) in the composite memristor-diode crossbar has been shown, as opposed to associative self-learning in existing hardware neural networks with discrete-memristors-based synapses. The change of crossbar cell’s output current caused by parasitic currents through adjacent cells has been determined. The results show that the control over Zenner diode characteristics allows reducing the power consumption of the composite crossbar. Obtained electrophysical characteristics prove the efficiency of the composite memristor-diode crossbar, intended for production of the memory and logic matrices.
Bobylev A. N., Udovichenko S. Yu., Busygin A. N., Ebrahim A. H. 2019 “Increase of switching range of resistive memristor for realization of a greater number of synaptic states in a neuroprocessor”. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, vol. 5, no. 2, pp. 124-136. DOI: 10.21684/2411-7978-2019-5-2-124-136 [In Russian]
Pisarev A. D., Busygin A. N., Bobylev A. N., Ebrahim A. H., Gubin A. A., Udovichenko S. Yu. 2019 “Materials selection and fabrication nanotechnology of the composite memristor-diode crossbar — the basis of neuroprocessor hardware implementation”. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, vol. 5, no. 4, pp. 200-219. DOI:10.21684/2411-7978-2019-5-4-200-219 [In Russian]
Pisarev A. D., Busygin A. N., Bobylev A. N., Udovichenko S. Yu 2017 “Combined memristor-diode crossbar as a memory storage base”. Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy, vol. 3, no. 4, pp. 142-149. DOI:10.21684/2411-7978-2017-3-4-142-149 [In Russian]
Bobylev A. N., Udovichenko S. Yu., Busygin A. N., Ebrahim A. H. 2020 “The effect of aluminum dopant amount in titania film on the memristor electrical properties”. Nano Hybrids and Composites, vol. 28, pp. 59-64. DOI: 10.4028/www.scientific.net/NHC.28.59
Filippov V. A., Bobylev A. N., Busygin A. N., Pisarev A. D., Udovichenko S. Yu. 2020 “A biomorphic neuron model and principles of designing a neural network with memristor synapses for a biomorphic neuroprocessor”. Neural Computing and Applications, vol. 32, pp. 2471–2485. DOI:10.1007/s00521-019-04383-7
Lee M.-J., Park Y., Kang B.-S., Ahn S.-E., Lee C., Kim K., Xianyu W., Stefanovich G., Lee J.-H., Chung S.-J., Kim Y.-H., Lee C.-S., Park J.-B., Baek I.-G., Yoo I.-K. 2007. “2-stack ID-IR cross-point structure with oxide diodes as switch elements for high density resistance RAM applications”. IEEE International Electron Devices Meeting, pp. 771-774. Washington, DC, USA. DOI: 10.1109/IEDM.2007.4419061
Maevsky O. V., Pisarev A. D., Busygin A. N., Udovichenko S. Yu. 2018 “Complementary memristor- diode cell for a memory matrix in neuromorphic processor”. International Journal of Nanotechnology, vol. 15, no. 4/5, pp. 388-393. DOI:10.1504/IJNT.2018.094795
Pisarev A. D., Busygin A. N., Udovichenko S. Yu., Maevsky O. V. 2018 “3D memory matrix based on a composite memristor-diode crossbar for a neuromorphic processor”. Microelectronic Engineering, vol. 198, pp. 1-7. DOI:10.1016/j.mee.2018.06.008
Pisarev A. D., Busygin A. N., Udovichenko S. Yu., Maevsky O. V. 2020 “The biomorphic neuroprocessor based on the composite memristor — diode crossbar”. Microelectronic Journal, vol. 102, art. 104827. DOI:10.1016/j.mejo.2020.104827
Udovichenko S. Yu., Pisarev A. D., Busygin A. N., Maevsky O. V. 2017 “3D CMOS, memristor nanotechnology for creating logical and memory matrices of neuroprocessor”. Nanoindustry, no. 5, pp. 26-34. DOI: 10.22184/1993-8578.2017.76.5.26.34
Udovichenko S. Yu., Pisarev A. D., Busygin A. N., Maevsky O. V. 2018 “Neuroprocessor based on combined memristor-diode crossbar”. Nanoindustry. no. 5. pp. 344-355. DOI:10.22184/1993-8578.2018.84.5.344.355.