Script list Publications
(1) Exploring Variability and Quantization Effects in Artificial Neural Networks using the MNIST Dataset
A. Blumenstein, E. Perez, Ch. Wenger, N. Dersch, A. Kloes, B. Iniguez, M. Schwarz
Solid-State Electronics 232, 109296 (2026)
DOI: 10.1016/j.sse.2025.109296
This paper investigates the impact of introducing variability to trained neural networks and examines the effects of variability and quantization on network accuracy. The study utilizes the MNIST dataset to evaluate various Multi-Layer Perceptron configurations: a baseline model with a Single-Layer Perceptron and an extended model with multiple hidden nodes. The effects of Cycle-to-Cycle variability on network accuracy are explored by varying parameters such as the standard deviation to simulate dynamic changes in network weights. In particular, the performance differences between the Single-Layer Perceptron and the Multi-Layer Perceptron with hidden layers are analyzed, highlighting the network’s robustness to stochastic perturbations. These results provide insights into the effects of quantization and network architecture on accuracy under varying levels of variability.
(2) RRAM-as-Reference Sensing with Parallelogram Crossbar Architecture for Large-Scale Arrays
R. Guo, S. Pechmann, A. Baroni, E. Perez, Ch. Wenger, P. Xu, A. Hagelauer
Proc. IEEE International Symposium on Circuits and Systems (ISCAS 2026), 2435 (2026)
DOI: 10.1109/ISCAS66217.2026.11562460, (LEOMEM)
(3) Deposition of CeOx/SnOx-Based Thin Films via RF Magnetron Sputtering for Resistive Gas Sensing Applications
A. Kalra, C.A. Chavarin, P. Nitsch, R. Tschammer, J.I. Flege, M. Ratzke, M.H. Zoellner, M.A. Schubert, Ch. Wenger, I.A. Fischer
Physica B: Condensed Matter 723, 418098 (2026)
DOI: 10.1016/j.physb.2025.418098, (iCampus II)
Cerium oxide-tin oxide (CeOx/SnOx) thin films with varying Sn content were deposited using RF magnetron sputtering and investigated for hydrogen sensing applications. Structural, compositional and morphological properties were characterized using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDX). Gas sensing measurements showed effective hydrogen detection at room temperature, with the sensitivity strongly influenced by Sn content and oxygen vacancy concentration. Higher Sn concentration enhanced the sensing response, which was correlated with microstructural features obtained from AFM and EDX, as well as with the presence of Ce3+ and Ce4+ oxidation states identified by XPS. This study highlights the potential of CeOx/SnOx thin films for possible back-end-of-line integration and provides proof-of-principle for room-temperature hydrogen sensing.
(4) Variability in HfO2-based Memristors under Pulse Operation
D. Maldonado, C. Acal, H. Ortiz, F. Navas-Gomez, A. Cantudo, Ch. Wenger, E. Pérez, J.B. Roldán
Microelectronic Engineering 304, 112445 (2026)
DOI: 10.1016/j.mee.2026.112445, (AVMMSafe)
We have studied device-to-device variability in TiN/Ti/HfO2/TiN devices under pulse operation. We measured extensively memristive devices that are CMOS integrated with different pulse trains, changing the pulse width and amplitude for groups of more than one hundred devices. The statistical parameters of the measured current were extracted to better understand the device physics under the pulse operation regime. An analytical model to describe synaptic depression and potentiation behavior in the device conductance is introduced, it fits accurately the means of the current data for all the pulse trains under study. In addition, an explanation of the measurements is enlightened with kinetic Monte Carlo simulations that allow the study of resistive switching at the atomic level. Finally, the probability distribution functions of the measured currents in some of the pulses within the pulse series employed are analyzed to extract the probability distribution that works better. A proposal for the implementation of device-to-device variability in the Stanford models is introduced.
(5) Formation and Role of the Interface Region in Ultrathin Metal Oxide Layers for Resistive Gas Sensors
C. Morales, R. Tschammer, D. Guttmann, C.A. Chavarin, K. Henkel, C. Wenger, J.I. Flege
Proc. iCampµs Cottbus Conference (iCCC 2026), 290 (2026)
DOI: 10.5162/iCCC2026/P42, (iCampus II)
(6) Electrical Characterization of CeO2/SnO2 Hydrogen Sensors: Influence of Temperature on Baseline Sensor Currents
A. Mudundi, A. Kalra, C. Morales, J.I. Flege, I.A. Fischer, C.A. Chavarin, Ch. Wenger
Proc. iCampus Cottbus Conference (iCCC 2026), 139 (2026)
DOI: 10.5162/iCCC2026/P5, (iCampus II)
(7) Entwicklung von Plasmonischen On-Chip Sensoren
S. Reiter, A. Sengül, P-G. Nitsch, F. Berkmann, C.A. Chavarin, J. Schlipf, Ch. Mai, Ch. Wenger, I.A. Fischer
Proc. iCampµs Cottbus Conference (iCCC 2026), 87 (2026)
DOI: 10.5162/iCCC2026/5.3, (iCampus II)
(8) Physics-Based RRAM Compact Model for Multilevel Programming Across Multiple Timescales
T. Zanotti, T. Rizzi, E. Perez Bosch-Quesada, A. Baroni, K.D.S. Reddy, E. Perez, Ch. Wenger, P. Pavan, F.M. Puglisi
Journal of Semiconductors 47(6), 062301 (2026)
DOI: 10.1088/1674-4926/25100024, (MuCoRe)
Resistive random access memories (RRAMs) are emerging as a key enabling technology for cost-effective, energy-efficient and secure chips, especially in the framework of edge computing. In particular, their electrically programmable resistance has been widely exploited in several in-memory computing and neuromorphic architectures. By adjusting the applied voltages and compliance currents (IC), RRAM devices can be programmed to multiple resistance states during set and reset procedures, enabling multilevel functionality. While the multilevel behavior of the reset phase is generally well captured by existing compact models, only a few account for the multilevel characteristics of the set operations. Moreover, such models are rarely validated against comprehensive experimental datasets capturing device dynamics across multiple timescales. In this work, we present a physics-based compact model that enhances the UniMORE RRAM framework by incorporating the dynamic lateral evolution of the conductive filament (CF), thereby enabling accurate simulation of set operations at varying IC values. The model is calibrated to experimental data from IHP 130 nm 1T1R RRAM technology and reproduces device behavior across several operating conditions using a single set of parameters. The results highlight the potential of the proposed compact model in design optimization workflows of RRAM-based circuits.
(9) Physics-Based RRAM Compact Model for Multilevel Programming Across Multiple Timescales
T. Zanotti, T. Rizzi, E. Perez Bosch-Quesada, A. Baroni, K.D.S. Reddy, E. Perez, Ch. Wenger, P. Pavan, F.M. Puglisi
Journal of Semiconductors 47(6), 062301 (2026)
DOI: 10.1088/1674-4926/25100024, (INSEKT)
Resistive random access memories (RRAMs) are emerging as a key enabling technology for cost-effective, energy-efficient and secure chips, especially in the framework of edge computing. In particular, their electrically programmable resistance has been widely exploited in several in-memory computing and neuromorphic architectures. By adjusting the applied voltages and compliance currents (IC), RRAM devices can be programmed to multiple resistance states during set and reset procedures, enabling multilevel functionality. While the multilevel behavior of the reset phase is generally well captured by existing compact models, only a few account for the multilevel characteristics of the set operations. Moreover, such models are rarely validated against comprehensive experimental datasets capturing device dynamics across multiple timescales. In this work, we present a physics-based compact model that enhances the UniMORE RRAM framework by incorporating the dynamic lateral evolution of the conductive filament (CF), thereby enabling accurate simulation of set operations at varying IC values. The model is calibrated to experimental data from IHP 130 nm 1T1R RRAM technology and reproduces device behavior across several operating conditions using a single set of parameters. The results highlight the potential of the proposed compact model in design optimization workflows of RRAM-based circuits.
(10) Time-Aware Modeling of RRAM Persistence in Closed-Loop RISC-V Intermittent Systems
H. Zhang, M. Fritscher, Ch. Wenger, D. Fey
Proc. 14th International Workshop on Energy Harvesting & Energy-Neutral Sensing Systems (ENSsys 2026), 56 (2026)
DOI: 10.1109/ENSsys71150.2026.00017, (HYB-RISC)
A. Blumenstein, E. Perez, Ch. Wenger, N. Dersch, A. Kloes, B. Iniguez, M. Schwarz
Solid-State Electronics 232, 109296 (2026)
DOI: 10.1016/j.sse.2025.109296
This paper investigates the impact of introducing variability to trained neural networks and examines the effects of variability and quantization on network accuracy. The study utilizes the MNIST dataset to evaluate various Multi-Layer Perceptron configurations: a baseline model with a Single-Layer Perceptron and an extended model with multiple hidden nodes. The effects of Cycle-to-Cycle variability on network accuracy are explored by varying parameters such as the standard deviation to simulate dynamic changes in network weights. In particular, the performance differences between the Single-Layer Perceptron and the Multi-Layer Perceptron with hidden layers are analyzed, highlighting the network’s robustness to stochastic perturbations. These results provide insights into the effects of quantization and network architecture on accuracy under varying levels of variability.
(2) RRAM-as-Reference Sensing with Parallelogram Crossbar Architecture for Large-Scale Arrays
R. Guo, S. Pechmann, A. Baroni, E. Perez, Ch. Wenger, P. Xu, A. Hagelauer
Proc. IEEE International Symposium on Circuits and Systems (ISCAS 2026), 2435 (2026)
DOI: 10.1109/ISCAS66217.2026.11562460, (LEOMEM)
(3) Deposition of CeOx/SnOx-Based Thin Films via RF Magnetron Sputtering for Resistive Gas Sensing Applications
A. Kalra, C.A. Chavarin, P. Nitsch, R. Tschammer, J.I. Flege, M. Ratzke, M.H. Zoellner, M.A. Schubert, Ch. Wenger, I.A. Fischer
Physica B: Condensed Matter 723, 418098 (2026)
DOI: 10.1016/j.physb.2025.418098, (iCampus II)
Cerium oxide-tin oxide (CeOx/SnOx) thin films with varying Sn content were deposited using RF magnetron sputtering and investigated for hydrogen sensing applications. Structural, compositional and morphological properties were characterized using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDX). Gas sensing measurements showed effective hydrogen detection at room temperature, with the sensitivity strongly influenced by Sn content and oxygen vacancy concentration. Higher Sn concentration enhanced the sensing response, which was correlated with microstructural features obtained from AFM and EDX, as well as with the presence of Ce3+ and Ce4+ oxidation states identified by XPS. This study highlights the potential of CeOx/SnOx thin films for possible back-end-of-line integration and provides proof-of-principle for room-temperature hydrogen sensing.
(4) Variability in HfO2-based Memristors under Pulse Operation
D. Maldonado, C. Acal, H. Ortiz, F. Navas-Gomez, A. Cantudo, Ch. Wenger, E. Pérez, J.B. Roldán
Microelectronic Engineering 304, 112445 (2026)
DOI: 10.1016/j.mee.2026.112445, (AVMMSafe)
We have studied device-to-device variability in TiN/Ti/HfO2/TiN devices under pulse operation. We measured extensively memristive devices that are CMOS integrated with different pulse trains, changing the pulse width and amplitude for groups of more than one hundred devices. The statistical parameters of the measured current were extracted to better understand the device physics under the pulse operation regime. An analytical model to describe synaptic depression and potentiation behavior in the device conductance is introduced, it fits accurately the means of the current data for all the pulse trains under study. In addition, an explanation of the measurements is enlightened with kinetic Monte Carlo simulations that allow the study of resistive switching at the atomic level. Finally, the probability distribution functions of the measured currents in some of the pulses within the pulse series employed are analyzed to extract the probability distribution that works better. A proposal for the implementation of device-to-device variability in the Stanford models is introduced.
(5) Formation and Role of the Interface Region in Ultrathin Metal Oxide Layers for Resistive Gas Sensors
C. Morales, R. Tschammer, D. Guttmann, C.A. Chavarin, K. Henkel, C. Wenger, J.I. Flege
Proc. iCampµs Cottbus Conference (iCCC 2026), 290 (2026)
DOI: 10.5162/iCCC2026/P42, (iCampus II)
(6) Electrical Characterization of CeO2/SnO2 Hydrogen Sensors: Influence of Temperature on Baseline Sensor Currents
A. Mudundi, A. Kalra, C. Morales, J.I. Flege, I.A. Fischer, C.A. Chavarin, Ch. Wenger
Proc. iCampus Cottbus Conference (iCCC 2026), 139 (2026)
DOI: 10.5162/iCCC2026/P5, (iCampus II)
(7) Entwicklung von Plasmonischen On-Chip Sensoren
S. Reiter, A. Sengül, P-G. Nitsch, F. Berkmann, C.A. Chavarin, J. Schlipf, Ch. Mai, Ch. Wenger, I.A. Fischer
Proc. iCampµs Cottbus Conference (iCCC 2026), 87 (2026)
DOI: 10.5162/iCCC2026/5.3, (iCampus II)
(8) Physics-Based RRAM Compact Model for Multilevel Programming Across Multiple Timescales
T. Zanotti, T. Rizzi, E. Perez Bosch-Quesada, A. Baroni, K.D.S. Reddy, E. Perez, Ch. Wenger, P. Pavan, F.M. Puglisi
Journal of Semiconductors 47(6), 062301 (2026)
DOI: 10.1088/1674-4926/25100024, (MuCoRe)
Resistive random access memories (RRAMs) are emerging as a key enabling technology for cost-effective, energy-efficient and secure chips, especially in the framework of edge computing. In particular, their electrically programmable resistance has been widely exploited in several in-memory computing and neuromorphic architectures. By adjusting the applied voltages and compliance currents (IC), RRAM devices can be programmed to multiple resistance states during set and reset procedures, enabling multilevel functionality. While the multilevel behavior of the reset phase is generally well captured by existing compact models, only a few account for the multilevel characteristics of the set operations. Moreover, such models are rarely validated against comprehensive experimental datasets capturing device dynamics across multiple timescales. In this work, we present a physics-based compact model that enhances the UniMORE RRAM framework by incorporating the dynamic lateral evolution of the conductive filament (CF), thereby enabling accurate simulation of set operations at varying IC values. The model is calibrated to experimental data from IHP 130 nm 1T1R RRAM technology and reproduces device behavior across several operating conditions using a single set of parameters. The results highlight the potential of the proposed compact model in design optimization workflows of RRAM-based circuits.
(9) Physics-Based RRAM Compact Model for Multilevel Programming Across Multiple Timescales
T. Zanotti, T. Rizzi, E. Perez Bosch-Quesada, A. Baroni, K.D.S. Reddy, E. Perez, Ch. Wenger, P. Pavan, F.M. Puglisi
Journal of Semiconductors 47(6), 062301 (2026)
DOI: 10.1088/1674-4926/25100024, (INSEKT)
Resistive random access memories (RRAMs) are emerging as a key enabling technology for cost-effective, energy-efficient and secure chips, especially in the framework of edge computing. In particular, their electrically programmable resistance has been widely exploited in several in-memory computing and neuromorphic architectures. By adjusting the applied voltages and compliance currents (IC), RRAM devices can be programmed to multiple resistance states during set and reset procedures, enabling multilevel functionality. While the multilevel behavior of the reset phase is generally well captured by existing compact models, only a few account for the multilevel characteristics of the set operations. Moreover, such models are rarely validated against comprehensive experimental datasets capturing device dynamics across multiple timescales. In this work, we present a physics-based compact model that enhances the UniMORE RRAM framework by incorporating the dynamic lateral evolution of the conductive filament (CF), thereby enabling accurate simulation of set operations at varying IC values. The model is calibrated to experimental data from IHP 130 nm 1T1R RRAM technology and reproduces device behavior across several operating conditions using a single set of parameters. The results highlight the potential of the proposed compact model in design optimization workflows of RRAM-based circuits.
(10) Time-Aware Modeling of RRAM Persistence in Closed-Loop RISC-V Intermittent Systems
H. Zhang, M. Fritscher, Ch. Wenger, D. Fey
Proc. 14th International Workshop on Energy Harvesting & Energy-Neutral Sensing Systems (ENSsys 2026), 56 (2026)
DOI: 10.1109/ENSsys71150.2026.00017, (HYB-RISC)
