NEUROTEC @ ICNCE 2026

SPIN-OFFS & STARTUP INITIATIVES

Start-up GRANTS

Demonstrator Chips

NEUROTEC-I

Material & Device Test Chip, 180nm XFAB CMOS, Compute-in-Memory, 1T1R Technology, Application: Accelerator for Kolmogorov Network

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 for Neuromorphic Systems

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

Nonlocal Monte Carlo via Reinforcement Learning

Read More: 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

Concepts for Neuromorphic Circuits

Read more: 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

Read more: 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

Read more: 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

Measurement and Test Technology for Neuromorphic Electronics

Read more: 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

Read more: 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

Read more: 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 and Modeling

Read More: 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

Read More: 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

Read More: 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

Component Integration and 3D Studies

Grenmyr, A. et al. Adv. Funct. Mater.35, no. 40 (2025): 2500512. https://doi.org/10.1002/adfm.202500512

A Novel Approach to Implementing Artificial Thalamic Neurons with Ferroelectric Transistors

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

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

Ahmad, R.W. et al. Adv. Intell. Syst.. 2025; 000, e202501018.

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

Material Development for Memristive Cells

Read More: 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

Read More: C. Sudarshan, O. Artner, P -P. Manea, et al. Advanced Intelligent Systems 8, e202501220 (2026)

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

Read More: 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

NEUROTEC @ ICNCE 2026

Last Modified: 25.06.2026