Bio inspired Memcomputing via Crossbar Structures



Prof. Thomas Mikolajick

Nr.: MI 1247/20-1

Chair of Nanoelectronics, TU Dresden


Dr.-Ing. Stefan Slesazeck

Nr.: SL 305/1-1

NaMLab gGmbH, Dresden


Prof. Ronald Tetzlaff

Nr.: TE 257/31-1

Chair for Fundamentals of Electrical Engineering, TU Dresden


An Analog Memristive and Memcapacitive Device for Neuromorphic Computing

Eter Mgeladze; Melanie Herzig; Richard Schroedter; Ronald Tetzlaff; Thomas Mikolajick; Stefan Slesazeck

2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 24-26 October 2022, Glasgow, United Kingdom

DOI: 10.1109/ICECS202256217.2022.9970915

Best Paper Award @ MOCAST22

Best Paper Award MOCAST 2022

SPICE Compact Model for an Analog Switching Niobium Oxide Memristor

Richard Schroedter; Ahmet Samil Demirkol; Alon Ascoli; Ronald Tetzlaff; Eter Mgeladze; Melanie Herzig; Stefan Slesazeck; Thomas Mikolajick

2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST), 08-10 June 2022, Bremen, Germany

DOI: 10.1109/MOCAST54814.2022.9837726

Physics-based modeling of a bi-layer Al₂O₃/Nb₂O₅ analog memristive device

Richard Schroedter; Eter Mgeladze; Melanie Herzig; Alon Ascoli; Stefan Slesazeck; Thomas Mikolajick; Ronald Tetzlaff

2022 IEEE International Symposium on Circuits and Systems (ISCAS), 27 May 2022 - 01 June 2022, Austin, TX, USA

DOI: 10.1109/ISCAS48785.2022.9937966

BioMCross Lab Tour @ NaMLab

BioMCross Lab Tour @ NaMLab

Project Description

The crossbar array, which realizes the matrix-vector multiplication (MVM) operation directly employing the Ohm law, represents the central building block of the non-conventional computing-in-memory (CIM) architectures, such as artificial neural networks (ANNs). The MVM is performed in an analog fashion, and, therefore, it is highly desirable to adopt non-volatile analog switching memristors, leveraging, furthermore, their memcapacitive properties.

In this project we aim at the exploitation of the interesting combined memristive and memcapacitive effects in Al2O3 / Nb2O3 based bi-layer device structures, recently developed at NaMLab to be used in conjunction with NFET transistors established at IHM. Based on these devices, we would like to develop a complete design methodology for the implementation of non-conventional versatile computations on a memristive/memcapacitive hardware, which utilizes a hybrid Memristor/Memcapacitor-CMOS architecture, mainly including MVM cores.

For this purpose, the proposed work will cover several essential aspects: (i) realization of cells integrating  memristive/memcapacitive devices and NFETs, (ii) experimental and theoretical modelling of the single cells, (iii) system-level modelling, analysis, design, and simulation of the MVM core-based ANNs, (iv) derivation of optimization algorithms, that account for the physical limitations of the hardware realization, (v) set-up of efficient instruction sets and peripheral circuitry design, and (vi) optimized mapping of memcomputing tasks onto the ANN structures. In this way, we expect to gain a comprehensive understanding of the feasibility of adopting the aforementioned device concepts in unconventional computing systems, such as multi-layer perceptrons (MLP), convolutional neural networks (CoNN), and recurrent neural networks (ReNN). Finally, we will investigate the possibility of implementing the Quadratic-Integrate-and-Fire (QIF) neuron model in hardware, and to demonstrate the richness of biomimetic dynamical phenomena, which an array of artificial neural structures of this kind may support.

Further involved scientists


Eter Mgeladze

NaMLab gGmbH, TU Dresden


Florian Nebe

Chair of Nanoelectronics, TU Dresden


Dr. Richard Schroedter

Chair of Fundamentals of Electrical Engineering, TU Dresden


Dr. Ahmet Samil Demirkol

Chair of Fundamentals of Electrical Engineering, TU Dresden


Dr. Alon Ascoli

Chair of Fundamentals of Electrical Engineering, TU Dresden


Dr. Benjamin Max

Chair of Nanoelectronics, TU Dresden