NEUROTEC

Neuro-inspirierte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft

AUSGRÜNDUNGEN & START-UP INITIATIVEN

Start-up Förderungen

Demonstrator Chips

NEUROTEC-I

Material & Device Test Chip, 180nm XFAB CMOS, Compute-in-Memory, 1T1R Technology

Demonstrator Chip OTTER

Compute-in-memory (CIM), ternary content-addressable-memory (TCAM) with VCM memristors BEOL integrated on 28nm CMOS (TSMC)

Demonstrator Chip Dolphin.DAD

Test Chip to optimize ADC and DACs for programming and read-out of memristors. Tape-Out: JAN 2024

Demonstrator Chip Dolphin.SNN

A Chip for research on coupled oscillators and their dynamics in real-world networks (e.g., network topologies, synchronization behavior), as well as potential applications in neuromorphic and edge computing contexts. Tape-Out: JUL 2024

Hardware-Software Integration für Neuromorphe Systeme

D. Dobrynin, M. Mohseni, J.P. Strachan, et al. arXiv:2508.10520 (2025)

Nonlocal Monte Carlo via Reinforcement Learning

N. Leroux, PP. Manea, C.Sudarshan, et al. Nature Computational Science v. 5, p. 813–824 (2025)

Analog in-memory computing attention mechanism for fast and energy-efficient large language models

J. Lohoff, E. Neftci, arXiv:2406.05027 (2025)

Optimizing Automatic Differentiation with Deep Reinforcement Learning

aktuelle publikationen

M. -J. Yang, Z. Yu, G. Pedretti, et al. IEEE 7th International Conference on Artificial Intelligence Circuits and Systems (AICAS) pp. 1-5 (2025)

Improved Memristor Control using Device Physics and Deep Reinforcement Learning

K. Schnieders, P. Bai, Y. Wanget, et al. IEEE Transactions on Electron Devices, vol. 72, no. 11, pp. 5988-5994 (2025)

Exploiting Read Noise of Filamentary VCM ReRAM for Robust TRNG

L. Pfeifer, A. Weiler, J. S. Haas, et al. IEEE Nordic Circuits and Systems Conference (NorCAS) pp. 1-7 (2025)

Thermal-EXAMINER: IP Extraction from MAGIC Logic-in-Memory Using Thermal Side-Channel Attacks

Mess- und Testtechnologie für Neuromorphe Elektronik

F. Munir, J. Klinkmann, P. Stasner, et al. IEEE Access, vol. 13, pp. 120762-120771 (2025)

Linear Potentiation in Filamentary Valence Change Mechanism With Sub-100 ps Pulses

T.S. Copetti, A. Chordia, M. Fieback, et al. Microelectronics Reliability, vol. 175 (2025)

Exploring the use of extreme temperatures to facilitate fault propagation in ReRAMs

D. Schön, F. Munir, P. Stasner, et al. IEEE Electron Device Letters, vol. 46, no. 11, pp. 2018-2021 (2025)

Exploiting thermal accumulation on sub-ns timescales for tuning potentiation of RRAM

Simulation und Modellierung

S. Son, A. Bende, D. Schönet, et al. IEEE Access, vol. 13, pp. 127280-12729 (2025)

A Comprehensive Compact Model for Multilevel Switching in TaOx-Based Memristive 1T-1R Cells

M. Buttberg, C. Schindler, I. Valov, et al. Advanced Material Technology vol. 11, no. 1 (2026): e00837

Current Compliance-Driven Morphological Evolution of Conductive Filaments in ECM Cells: Mechanisms, Switching Regimes, and Thermal Effects

S. Menzel, B. Kersting, R. W. Ahmad, et al. Advanced Electronic Materials vol. 12, no. 1 (2026): e00496

A Device-Level Compact Model for Mushroom-Type Phase Change Memory

Bauteilintegration und 3D-Studien

A. Grenmyr, J. Zhang, J. Sun, et al. Adv. Funct. Mater.35, no. 40 (2025): 2500512.

A Novel Approach to Implementing Artificial Thalamic Neurons with Ferroelectric Transistors

O. Artner, F. Cüppers, S. Son, et al. Advanced Electronic Materials (2026): e00891.

The Influence of Residual Ion Drift During Programming of Chip-Integrated Nanoscale HfO2-Based Memristive Devices

C. Sudarshan, O. Artner, P. Manea, et al., Advanced Intelligent Systems2026, 8, e202501220.

A Flexible and Energy-Efficient Compute-in-Memory Accelerator for Kolmogorov–Arnold Networks

Materialentwicklung für Memristive Zellen

K. Ran, J. Jo, S. Cruces, et al., Nat Commun 16, 7433 (2025).

Unraveling the dynamics of conductive filaments in MoS2-based memristors by operando transmission electron microscopy

A. Espiari., H. Padberg, A. Kiehn, et al. Materials vol. 18, 4681 (2025)

The Influence of Reactive Ion Etching Chemistry on the Initial Resistance and Cycling Stability of Line-Type (Bridge) Phase-Change Memory Devices

R.W. Ahmad, S. Chekol, J. Rasbach, et al., Adv. Intell. Syst. (2025); 000, e202501018.

Compact Modeling of Volatile-Switching Electrochemical Metallization Memory Cells by Means of the Electromotive Force

NEUROTEC: Neuro-inspirerte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft

Letzte Änderung: 14.07.2026