trudyniisiТруды НИИСИSRISA Proceedings2225-73493033-6422НИЦ «КУРЧАТОВСКИЙ ИНСТИТУТ» - НИИСИtrudyniisi-94Research ArticleПРОЕКТИРОВАНИЕ И МОДЕЛИРОВАНИЕ СБИСDESIGN AND MODELING OF VLSIПоточечная запись информации в резисторную матрицуLocal Point Recording of Information into a Crossbar Resistor ArrayКотовВ. Б.KotovV. B.

Москва

kotov.vlb@yandex.ruБесхлебноваГ. А.BeskhlebnovaG. A.

Москва

gab19@list.ruНИЦ «Курчатовский институт» – НИИСИРоссия2024091220251443340Copyright © Котов В.Б., Бесхлебнова Г.А., 20252025Котов В.Б., Бесхлебнова Г.А.Kotov V.B., Beskhlebnova G.A.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.t-niisi.ru/jour/article/view/94

Для большой резисторной матрицы типа кроссбарр локальная запись информации в выбранный резистор (то есть изменение проводимости этого резистора) сталкивается с трудностями, связанными с ограниченным числом управляющих сигналов – напряжений на проводниках структуры. Поскольку число проводников значительно меньше числа резисторов, при подаче напряжения на целевой резистор, возникают напряжения на многих нецелевых резисторах. Соответствующие изменения проводимостей нецелевых резисторов необходимо компенсировать. В работе рассмотрена процедура записи с использованием высокочастотных гармонических сигналов и с управлением адресации с помощью варьирования сопротивлений резисторов подключения. На основе анализа с использованием модели простого резисторного элемента показана возможность точечной записи информации в резисторную матрицу, то есть локального изменения матрицы проводимостей. Обсуждаются условия, обеспечивающие выполнимость и удобство такой процедуры.

For a large crossbar resistor array, local recording of information into the selected variable resistor (that is, changing the conductivity of this resistor) faces difficulties associated with a limited number of control signals - voltages on the conductors of the structure. Since the number of conductors is significantly less than the number of resistors, when voltage is applied to the target resistor, voltages arise on many non-target resistors. The corresponding changes in the conductivities of non-target resistors must be compensated. The paper considers the recording procedure using high-frequency harmonic signals and with addressing control by varying the resistances of the connection resistors. Based on the analysis using the model of a simple resistor element, the possibility of point recording of information into a resistor array, that is a local change in the conductivity matrix, is shown. The conditions ensuring the feasibility and convenience of such a procedure are discussed.

переменный резисторвысокочастотный сигналрезисторная матрицаматрица проводимостейпоточечная записьvariable resistorhigh-frequency signalcrossbar resistor arrayconductivity matrixpoint recordingРабота выполнена в рамках государственного задания ФГУ ФНЦ НИИСИ РАН по теме № FNEF-2024-0001 "Создание и реализация доверенных систем искусственного интеллекта, основанных на новых математических и алгоритмических методах, моделях быстрых вычислений, реализуемых на отечественных вычислительных системах" (1023032100070-3-1.2.1).References2015.Adamatzky A., Chua L. Memristor Networks. Springer International Publishing (2014).2015.Adamatzky A., Chua L. Memristor Networks. Springer International Publishing (2014).Advances in Memristors, Memristive Devices and Systems. /Edited by S. Vaidyanathan and C. Volos. Springer In-ternational Publishing AG (2017).Advances in Memristors, Memristive Devices and Systems. /Edited by S. Vaidyanathan and C. Volos. Springer In-ternational Publishing AG (2017).Kim S. 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(Eds.). Advances in Neural Computation, Machine Learning, and Cognitive Research V (NEUROINFORMATICS 2021). Studies in Computational Intelligence. Vol. 1008. Cham: Springer. 2022. PP. 276-284.Kotov V.B., Beskhlebnova G.A. Generation of the Conductivity Matrix. //B. Kryzhanovsky et al. (Eds.). Advances in Neural Computation, Machine Learning, and Cognitive Research V (NEUROINFORMATICS 2021). Studies in Computational Intelligence. Vol. 1008. Cham: Springer. 2022. PP. 276-284.Beskhlebnova G.A., Kotov V.B. The Variable Resistor Under a High-Frequency Signal. //B. Kryzhanovsky et al. (Eds.). Advances in Neural Computation, Machine Learning, and Cognitive Research VII (NEUROINFORMATICS 2023). Studies in Computational Intelligence. Vol. 1120. Springer Nature Switzerland AG. 2023. PP. 257–266. https://doi.org/10.1007/978-3-031-44865-2_28.Beskhlebnova G.A., Kotov V.B. The Variable Resistor Under a High-Frequency Signal. //B. Kryzhanovsky et al. (Eds.). Advances in Neural Computation, Machine Learning, and Cognitive Research VII (NEUROINFORMATICS 2023). Studies in Computational Intelligence. Vol. 1120. Springer Nature Switzerland AG. 2023. PP. 257–266. https://doi.org/10.1007/978-3-031-44865-2_28.Kotov V.B., Beskhlebnova G.A. Use of High-Frequency Signals to Generate a Conductivity Matrix //B. Kryzhanovsky et al. (Eds.). Advances in Neural Computation, Machine Learning, and Cognitive Research VIII (NEU-ROINFORMATICS 2024). Studies in Computational Intelligence. Cham: Springer. 2024 (to be published).Kotov V.B., Beskhlebnova G.A. Use of High-Frequency Signals to Generate a Conductivity Matrix //B. Kryzhanovsky et al. (Eds.). Advances in Neural Computation, Machine Learning, and Cognitive Research VIII (NEU-ROINFORMATICS 2024). Studies in Computational Intelligence. Cham: Springer. 2024 (to be published).Kotov V.B., Yudkin F.A. Modeling and Characterization of Resistor Elements for Neuromorphic Systems. 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The authors declare that there are no conflicts of interest present.