@misc{BlumensteinPerezWengeretal., author = {Blumenstein, Alan and P{\´e}rez, Eduardo and Wenger, Christian and Dersch, Nadine and Kloes, Alexander and I{\~n}{\´i}guez, Benjam{\´i}n and Schwarz, Mike}, title = {Evaluating device variability in RRAM-based single- and multi-layer perceptrons}, series = {2025 32nd International Conference on Mixed Design of Integrated Circuits and System (MIXDES)}, journal = {2025 32nd International Conference on Mixed Design of Integrated Circuits and System (MIXDES)}, publisher = {IEEE}, address = {New York}, isbn = {978-83-63578-27-5}, doi = {10.23919/MIXDES66264.2025.11092102}, pages = {74 -- 77}, abstract = {This work investigates the impact of stochastic weight variations in hardware implementations of artificial neural networks, focusing on a Single-Layer Perceptron and Multi-Layer Perceptrons. A variable neural network model is introduced, applying Gaussian variability to synaptic weights based on an adjustment rate, which controls the proportion of affected weights. By studying how stochastic variations affect accuracy, simulations under device-to-device and cycle-to-cycle variation conditions demonstrate that Single-Layer Perceptrons are more sensitive to weight variations, while Multi-Layer perceptrons show greater robustness. Additionally, stochastic quantization improves the performance of Multi-Layer Perceptrons but has minimal effect on Single-Layer Perceptrons.}, language = {en} } @misc{DubeyRajuLukoseetal., author = {Dubey, Pawan Kumar and Raju, Ashraful Islam and Lukose, Rasuole and Wenger, Christian and Lukosius, Mindaugas}, title = {Optimizing graphene ring modulators : a comparative study of straight, bent, and racetrack geometries}, series = {Nanomaterials}, volume = {15}, journal = {Nanomaterials}, number = {15}, publisher = {MDPI AG}, address = {Basel}, issn = {2079-4991}, doi = {10.3390/nano15151158}, pages = {1 -- 17}, abstract = {Graphene-based micro-ring modulators are promising candidates for next-generation optical interconnects, offering compact footprints, broadband operation, and CMOS compatibility. However, most demonstrations to date have relied on conventional straight bus coupling geometries, which limit design flexibility and require extremely small coupling gaps to reach critical coupling. This work presents a comprehensive comparative analysis of straight, bent, and racetrack bus geometries in graphene-on-silicon nitride (Si₃N₄) micro-ring modulators operating near 1.31 µm. Based on finite-difference time-domain simulation results, a proposed racetrack-based modulator structure demonstrates that extending the coupling region enables critical coupling at larger gaps—up to 300 nm—while preserving high modulation efficiency. With only 6-12\% graphene coverage, this geometry achieves extinction ratios of up to 28 dB and supports electrical bandwidths approaching 90 GHz. Findings from this work highlight a new co-design framework for coupling geometry and graphene coverage, offering a pathway to high-speed and high-modulation-depth graphene photonic modulators suitable for scalable integration in next-generation photonic interconnects devices.}, language = {en} } @misc{KalraAlvaradoChavarinNitschetal., author = {Kalra, Amanpreet and Alvarado Chavarin, Carlos and Nitsch, Paul-Gregor and Tschammer, Rudi and Flege, Jan Ingo and Ratzke, Markus and Zoellner, Marvin Hartwig and Schubert, Markus Andreas and Wenger, Christian and Fischer, Inga Anita}, title = {Deposition of CeOₓ/SnOₓ-based thin films via RF magnetron sputtering for resistive gas sensing applications}, series = {Physica B, Condensed matter}, volume = {723}, journal = {Physica B, Condensed matter}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {0921-4526}, doi = {10.1016/j.physb.2025.418098}, pages = {1 -- 7}, abstract = {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.}, language = {en} } @misc{WenBaroniUhlmannetal., author = {Wen, Jianan and Baroni, Andrea and Uhlmann, Max and Perez, Eduardo and Wenger, Christian and Krstic, Milos}, title = {ReFFT : an energy-efficient RRAM-based FFT accelerator}, series = {IEEE transactions on computer-aided design of integrated circuits and systems}, journal = {IEEE transactions on computer-aided design of integrated circuits and systems}, publisher = {IEEE}, address = {Piscataway, NJ}, issn = {0278-0070}, doi = {10.1109/TCAD.2025.3627146}, pages = {1 -- 14}, abstract = {The fast Fourier transform (FFT) is a highly efficient algorithm for computing the discrete Fourier transform (DFT). It is widely employed in various applications, including digital communication, image processing, and signal analysis. Recently, in-memory computing architectures based on emerging technologies, such as resistive RAM (RRAM), have demonstrated promising performance with low hardware cost for data-intensive applications. However, directly mapping FFT onto RRAM crossbars is challenging because the algorithm relies on many small, sequential butterfly operations, while cross-bars are optimized for large-scale, highly parallel vector-matrix multiplications (VMMs). In this paper, we introduce ReFFT, a system architecture that reformulates FFT computations for efficient execution on RRAM crossbars. ReFFT combines the reduced computational complexity of FFT with the parallel VMM capability of RRAM. We incorporate measured device data into our framework to analyze the effect of variability and develop an adaptive mapping scheme that improves twiddle-factor programming accuracy, leading to a 9.9 dB peak signal-to-noise ratio (PSNR) improvement for a 256-point FFT. Compared with prior RRAM-based DFT designs, ReFFT achieves up to 4.6× and 19.5× higher energy efficiency for 256- and 2048-point FFTs, respectively. The system is further validated in digital communication and satellite image compression tasks.}, language = {en} } @misc{DerschPerezWengeretal., author = {Dersch, Nadine and Perez, Eduardo and Wenger, Christian and Lanza, Mario and Zhu, Kaichen and Schwarz, Mike and I{\~n}{\´i}guez, Benjam{\´i}n and Kloes, Alexander}, title = {Statistical model for the calculation of conductance variations of memristive devices}, series = {2025 IEEE European Solid-State Electronics Research Conference (ESSERC)}, journal = {2025 IEEE European Solid-State Electronics Research Conference (ESSERC)}, publisher = {IEEE}, address = {Piscataway, NJ}, doi = {10.1109/ESSERC66193.2025.11213973}, pages = {373 -- 376}, abstract = {This paper presents a statistical model which calculates the expected conductance variations from device to device or from cycle to cycle of memristive devices. The mean readout current and its standard deviation can be calculated for binary and multi-level devices. These values are important for simulating hardware-based artificial neural networks at circuit level and testing their functionality. Research into hardwarebased artificial neural networks is important because they are energy-efficient. Furthermore to calculating the variations, the statistical model can be used to determine what influence the cumulative distribution function of switching has on the variations and which behavior provides the best results for the hardwarebased artificial neural network. Some memristive devices exhibit multi-level behavior due to defects in the switching layer. The number of these defects and the optimal amount can be estimated.}, language = {en} } @misc{DerschPerezWengeretal., author = {Dersch, Nadine and Perez, Eduardo and Wenger, Christian and Schwarz, Mike and Iniguez, Benjamin and Kloes, Alexander}, title = {A closed-form model for programming of oxide-based resistive random access memory cells derived from the Stanford model}, series = {Solid-state electronics}, volume = {230}, journal = {Solid-state electronics}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {0038-1101}, doi = {10.1016/j.sse.2025.109238}, pages = {1 -- 5}, abstract = {This paper presents a closed-form model for pulse-based programming of oxide-based resistive random access memory devices. The Stanford model is used as a basis and solved in a closed-form for the programming cycle. A constant temperature is set for this solution. With the closed-form model, the state of the device after programming or the required programming settings for achieving a specific device conductance can be calculated directly and quickly. The Stanford model requires time-consuming iterative calculations for high accuracy in transient analysis, which is not necessary for the closed-form model. The closed-form model is scalable across different programming pulse widths and voltages.}, language = {en} } @misc{WenBaroniMistronietal., author = {Wen, Jianan and Baroni, Andrea and Mistroni, Alberto and Perez, Eduardo and Zambelli, Cristian and Wenger, Christian and Krstic, Milos and Bolzani P{\"o}hls, Leticia Maria}, title = {ReDiM : an efficient strategy for read disturb mitigation in RRAM-based accelerators}, series = {2025 IEEE 31st International Symposium on On-Line Testing and Robust System Design (IOLTS)}, journal = {2025 IEEE 31st International Symposium on On-Line Testing and Robust System Design (IOLTS)}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {979-8-3315-3334-2}, doi = {10.1109/IOLTS65288.2025.11117065}, pages = {1 -- 7}, abstract = {Resistive RAM (RRAM) has emerged as a promising non-volatile memory technology for implementing energy-efficient hardware accelerators within the in-memory computing (IMC) paradigm. However, due to the immature fabrication process and inherent material instabilities, frequent read operations during computations can induce read disturb effects, leading to unintended resistance drift and potential data corruption. Existing mitigation approaches primarily focus on detecting read disturb effects and triggering memory refresh operations. In this work, we propose an architecture-level solution that mitigates read disturb in RRAM-based accelerators. Our strategy employs crossbar duplication and decomposes the single high input pulse into two lower-amplitude pulses, effectively minimizing the risk of read disturb. To validate our approach, we develop a simulation framework that incorporates measurement data from characterized RRAM devices under read disturb stress conditions. Experimental results on VGG-8 with CIFAR-10 demonstrate that the proposed method significantly mitigates inference accuracy degradation caused by read disturb in RRAM-based accelerators, while incurring modest area and energy overheads of 12.32\% and 2.15\%, respectively. This work provides a practical and scalable solution for enhancing the robustness of RRAM-based accelerators in edge and high-performance computing applications.}, language = {en} } @misc{AftowiczFritscherLehnigeretal., author = {Aftowicz, Marcin and Fritscher, Markus and Lehniger, Kai and Wenger, Christian and Langend{\"o}rfer, Peter and Brzozowski, Marcin}, title = {Hardware-friendly Nystr{\"o}m approximation for water treatment anomaly detection}, series = {IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society : proceedings}, journal = {IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society : proceedings}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {Piscataway, NJ}, isbn = {978-1-6654-6454-3}, doi = {10.1109/IECON55916.2024.10905880}, pages = {1 -- 7}, abstract = {This paper presents an approach to accelerate One-Class Support Vector Machines (SVM) using a hardware-friendly kernel that doesn't rely on multiplication operations, thus adaptable to hardware platforms. Leveraging Nystr{\"o}m approximation, we implemented a pipeline and compared its performance against a software implementation using libsvm. Furthermore, we evaluated the efficiency of our approach by deploying it on an FPGA. Our experiments, conducted on the SWaT dataset, demonstrate a 50x speedup using the FPGA implementation, achieving a classification time of 21 microseconds per instance. Importantly, we find no degradation in performance, as measured by the f-score of the attack class in the test set. This study explores the potential of hardware acceleration in optimizing anomaly detection systems for real-time applications.}, language = {en} } @misc{BlumensteinPerezWengeretal., author = {Blumenstein, Alan and P{\´e}rez, Eduardo and Wenger, Christian and Dersch, Nadine and Kloes, Alexander and I{\~n}{\´i}guez, Benjam{\´i}n and Schwarz, Mike}, title = {Exploring variability and quantization effects in artificial neural networks using the MNIST dataset}, series = {Solid-state electronics}, volume = {232}, journal = {Solid-state electronics}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {0038-1101}, doi = {10.1016/j.sse.2025.109296}, pages = {1 -- 4}, abstract = {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.}, language = {en} } @misc{PerezMaldonadoPechmannetal., author = {Perez, Eduardo and Maldonado, David and Pechmann, Stefan and Reddy, Keerthi Dorai Swamy and Uhlmann, Max and Hagelauer, Amelie and Roldan, Juan Bautista and Wenger, Christian}, title = {Impact of the series resistance on switching characteristics of 1T1R HfO₂-based RRAM devices}, series = {2025 15th Spanish Conference on Electron Devices (CDE)}, journal = {2025 15th Spanish Conference on Electron Devices (CDE)}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {Piscataway, NJ}, isbn = {979-8-3315-9618-7}, doi = {10.1109/CDE66381.2025.11038868}, pages = {1 -- 4}, abstract = {This study investigates the influence of the series resistance (RS) on the switching characteristics of 1-transistor-1-resistor (1T1R) RRAM devices based on HfO2 and Al:HfO₂ dielectrics. Intrinsic RS values were extracted from I-V characteristics measured over 50 Reset-Set cycles at various gate voltages (VG) by using a numerical transformation method. Results reveal the contribution of the transistor's resistance to the overall RS. A linear relationship between RS values and Set transition voltages (VTS) was found, with larger RS values amplifying the variability in switching parameters. Comparative analysis of cumulative distribution functions (CDFs) highlights differences between technologies, showing lower VTS values as well as lower sensitivity to RS for Al:HfO₂-based devices. These findings underscore the critical role of RS in modeling and optimizing the performance of RRAM devices for reliable operation.}, language = {en} } @misc{MoralesTschammerGuttmannetal., author = {Morales, Carlos and Tschammer, Rudi and Guttmann, Dominic and Chavarin, Carlos Alvarado and Ruffert, Christine and Henkel, Karsten and Wenger, Christian and Flege, Jan Ingo}, title = {Bottom-up strategy to develop ultrathin active layers by atomic layer deposition for room temperature hydrogen sensors compatible with CMOS technology}, series = {MikroSystemTechnik Kongress 2025 : Mikroelektronik, Mikrosystemtechnik und ihre Anwendungen - Nachhaltigkeit und Technologiesouver{\"a}nit{\"a}t : proceedings : 27.-29. Oktober 2025, Duisburg}, journal = {MikroSystemTechnik Kongress 2025 : Mikroelektronik, Mikrosystemtechnik und ihre Anwendungen - Nachhaltigkeit und Technologiesouver{\"a}nit{\"a}t : proceedings : 27.-29. Oktober 2025, Duisburg}, publisher = {VDE VERLAG GmbH}, address = {Berlin}, isbn = {978-3-8007-6614-7}, pages = {71ff.}, language = {en} }