TY - GEN A1 - Soltani Zarrin, Pouya A1 - Rockendorf, Niels A1 - Wenger, Christian T1 - In-Vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Machine Learning Tools T2 - IEEE Access N2 - Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease and a major cause of morbidity and mortality worldwide. Although a curative therapy has yet to be found, permanent monitoring of biomarkers that reflect the disease progression plays a pivotal role for the effective management of COPD. The accurate examination of respiratory tract fluids like saliva is a promising approach for staging the disease and predicting its upcoming exacerbations in a Point-of-Care (PoC) environment. Nonetheless, this approach is only feasible by concurrent consideration of patients' demographic and medical parameters. Therefore, Machine Learning (ML) tools are necessary for the comprehensive recognition of COPD in a PoC setting. As a result, the objective of this work was to implement ML tools on the data acquired from characterizing saliva samples of COPD patients and healthy controls for classification purposes. First, a permittivity biosensor was used to characterize dielectric properties of saliva samples and, subsequently, ML tools were applied on the acquired data for classification. The XGBoost gradient boosting algorithm provided a high classification accuracy of 91.25%, making it a promising model for COPD recognition. Integration of this model on a neuromorphic chip, in the future, will enable the real-time detection of COPD in PoC, with low energy consumption and high patient privacy. KW - COPD KW - Machine learning KW - Point of care Y1 - 2020 U6 - https://doi.org/10.1109/ACCESS.2020.3023971 SN - 2169-3536 VL - Vol. 8 SP - 168053 EP - 168060 ER - TY - GEN A1 - Bogun, Nicolas A1 - Perez-Bosch Quesada, Emilio A1 - Perez, Eduardo A1 - Wenger, Christian A1 - Kloes, Alexander A1 - Schwarz, Mike T1 - Analytical Calculation of Inference in Memristor-based Stochastic Artificial Neural Networks T2 - 29th International Conference on Mixed Design of Integrated Circuits and System (MIXDES), 23-24 June 2022 , Wrocław, Poland N2 - The impact of artificial intelligence on human life has increased significantly in recent years. However, as the complexity of problems rose aswell, increasing system features for such amount of data computation became troublesome due to the von Neumann’s computer architecture. Neuromorphic computing aims to solve this problem by mimicking the parallel computation of a human brain. For this approach, memristive devices are used to emulate the synapses of a human brain. Yet, common simulations of hardware based networks require time consuming Monte-Carlo simulations to take into account the stochastic switching of memristive devices. This work presents an alternative concept making use of the convolution of the probability distribution functions (PDF) of memristor currents by its equivalent multiplication in Fourier domain. An artificial neural network is accordingly implemented to perform the inference stage with handwritten digits. KW - RRAM KW - neural network Y1 - 2022 SN - 978-83-63578-22-0 SN - 978-83-63578-21-3 SN - 978-1-6654-6176-4 U6 - https://doi.org/10.23919/MIXDES55591.2022.9838321 SP - 83 EP - 88 ER - TY - GEN A1 - Soltani Zarrin, Pouya A1 - Zahari, Finn A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Perez, Eduardo A1 - Kohlstedt, Hermann A1 - Wenger, Christian T1 - Neuromorphic on‑chip recognition of saliva samples of COPD and healthy controls using memristive devices T2 - Scientific Reports N2 - Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. However, shortcomings of cloud-based ML tools in terms of data safety and energy efficiency limit their integration with low-power medical devices. To address this, energy efficient neuromorphic platforms can be used for the hardware-based implementation of ML methods. Therefore, a memristive neuromorphic platform is presented in this paper for the on-chip recognition of saliva samples of COPD patients and healthy controls. The results of its performance evaluations showed that the digital neuromorphic chip is capable of recognizing unseen COPD samples with accuracy and sensitivity values of 89% and 86%, respectively. Integration of this technology into personalized healthcare devices will enable the better management of chronic diseases such as COPD. KW - RRAM KW - memristive device KW - neural network Y1 - 2020 U6 - https://doi.org/10.1038/s41598-020-76823-7 SN - 2045-2322 VL - 10 ER - TY - GEN A1 - Perez, Eduardo A1 - Maldonado, David A1 - Acal, Christian A1 - Ruiz-Castro, Juan Eloy A1 - Aguilera, Ana María A1 - Jimenez-Molinos, Francisco A1 - Roldan, Juan Bautista A1 - Wenger, Christian T1 - Advanced Temperature Dependent Statistical Analysis of Forming Voltage Distributions for Three Different HfO2-Based RRAM Technologies T2 - Solid State Electronics N2 - In this work, voltage distributions of forming operations are analyzed by using an advanced statistical approach based on phase-type distributions (PHD). The experimental data were collected from batches of 128 HfO2-based RRAM devices integrated in 4-kbit arrays. Three di erent switching oxides, namely, polycrystalline HfO2, amorphous HfO2, and Al-doped HfO2, were tested in the temperature range from -40 to 150 oC. The variability of forming voltages has been usually studied by using the Weibull distribution (WD). However, the performance of the PHD analysis demonstrated its ability to better model this crucial operation. The capacity of the PHD to reproduce the experimental data has been validated by means of the Kolmogorov-Smirnov test, while the WD failed in many of the cases studied. In addition, PHD allows to extract information about intermediate probabilistic states that occur in the forming process and the transition probabilities between them; in this manner, we can deepen on the conductive lament formation physics. In particular, the number of intermediate states can be related to the device variability. KW - RRAM KW - HfO2 Y1 - 2021 SN - 0038-1101 SN - 1879-2405 VL - 176 ER - TY - GEN A1 - Lukosius, Mindaugas A1 - Lukose, Rasuolė A1 - Lisker, Marco A1 - Luongo, G. A1 - Elviretti, M. A1 - Mai, Andreas A1 - Wenger, Christian T1 - Graphene Research in 200 mm CMOS Pilot Line T2 - 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO), 2022 N2 - Due to the unique electronic structures, graphene and other 2D Materials are considered as materials which can enable and extend the functionalities and performance in a large variety of applications, among them in microelectronics. At this point, the investigation and preparation of graphene devices in conditions resembling as close as possible the Si technology environment is of highest importance.Towards these goals, this paper focuses on the full spectra of graphene research aspects in 200mm pilot line. We investigated different process module developments such as CMOS compatible growth of high quality graphene on germanium and its growth mechanisms, transfer related challenges on target substrates, patterning, passivation and various concepts of contacting of graphene on a full 200 mm wafers. Finally, we fabricated proof-of-concept test structures e.g. TLM, Hall bars and capacitor structures to prove the feasibility of graphene processing in the pilot line of IHP. KW - Graphene KW - CMOS Y1 - 2022 SN - 978-953-233-103-5 SN - 978-953-233-102-8 SN - 978-1-6654-8434-3 U6 - https://doi.org/10.23919/MIPRO55190.2022.9803362 SN - 2623-8764 SN - 1847-3938 SP - 113 EP - 117 ER - TY - GEN A1 - Baroni, Andrea A1 - Zambelli, Cristian A1 - Olivo, Piero A1 - Perez, Eduardo A1 - Wenger, Christian A1 - Ielmini, Daniele T1 - Tackling the Low Conductance State Drift through Incremental Reset and Verify in RRAM Arrays T2 - 2021 IEEE International Integrated Reliability Workshop (IIRW), South Lake Tahoe, CA, USA, 10 December 2021 N2 - Resistive switching memory (RRAM) is a promising technology for highly efficient computing scenarios. RRAM arrays enabled the acceleration of neural networks for artificial intelligence and the creation of In-Memory Computing circuits. However, the arrays are affected by several issues materializing in conductance variations that might cause severe performance degradation in those applications. Among those, one is related to the drift of the low conductance states appearing immediately at the end of program and verify algorithms that are fundamental for an accurate Multi-level conductance operation. In this work, we tackle the issue by developing an Incremental Reset and Verify technique showing enhanced variability and reliability features compared with a traditional refresh-based approach. KW - RRAM KW - resistive switching KW - neural network Y1 - 2021 SN - 978-1-6654-1794-5 SN - 978-1-6654-1795-2 U6 - https://doi.org/10.1109/IIRW53245.2021.9635613 SN - 2374-8036 PB - Institute of Electrical and Electronics Engineers (IEEE) ER - TY - GEN A1 - Milo, Valerio A1 - Anzalone, Francesco A1 - Zambelli, Cristian A1 - Perez, Eduardo A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Ossorio, Óscar G. A1 - Olivo, Piero A1 - Wenger, Christian A1 - Ielmini, Daniele T1 - Optimized programming algorithms for multilevel RRAM in hardware neural networks T2 - IEEE International Reliability Physics Symposium (IRPS), 2021 N2 - A key requirement for RRAM in neural network accelerators with a large number of synaptic parameters is the multilevel programming. This is hindered by resistance imprecision due to cycle-to-cycle and device-to-device variations. Here, we compare two multilevel programming algorithms to minimize resistance variations in a 4-kbit array of HfO 2 RRAM. We show that gate-based algorithms have the highest reliability. The optimized scheme is used to implement a neural network with 9-level weights, achieving 91.5% (vs. software 93.27%) in MNIST recognition. KW - RRAM KW - Multilevel switching KW - neural network KW - memristive switching Y1 - 2021 SN - 978-1-7281-6894-4 U6 - https://doi.org/10.1109/IRPS46558.2021.9405119 SN - 1938-1891 ER - TY - GEN A1 - Strobel, Carsten A1 - Alvarado Chavarin, Carlos A1 - Völkel, Sandra A1 - Jahn, Andreas A1 - Hiess, Andre A1 - Knaut, Martin A1 - Albert, Matthias A1 - Wenger, Christian A1 - Steinke, Olaff A1 - Stephan, Ulf A1 - Röhlecke, Sören A1 - Mikolajick, Thomas T1 - Enhanced Electrical Properties of Optimized Vertical Graphene-Base Hot Electron Transistors T2 - ACS Applied Electronic Materials N2 - The arrival of high-mobility two-dimensional materials like graphene leads to the renaissance of former vertical semiconductor–metal–semiconductor (SMS) hot electron transistors. Because of the monolayer thickness of graphene, improved SMS transistors with a semimetallic graphene-base electrode are now feasible for high-frequency applications. In this study we report about a device that consists of amorphous silicon, graphene, and crystalline silicon. For the first time, this device is fabricated by a four-mask lithography process which leads to significant improvements in the device performance. A strongly increased common-emitter current gain of 2% could be achieved while the on–off ratio improved to 1.6 × 105, which is already higher than predicted theoretically. This could be mainly attributed to better interface characteristics and decreased lateral dimensions of the devices. A cutoff frequency of approximately 26 MHz could be forecasted based on the DC measurements of the device. KW - Graphene KW - Transistor Y1 - 2023 U6 - https://doi.org/10.1021/acsaelm.2c01725 SN - 2637-6113 VL - 5 IS - 3 SP - 1670 EP - 1675 ER - TY - GEN A1 - Mai, Christian A1 - Marschmeyer, Steffen A1 - Peczek, Anna A1 - Kroh, Aleksandra A1 - Jose, Josmy A1 - Reiter, Sebastian A1 - Fischer, Inga Anita A1 - Wenger, Christian A1 - Mai, Andreas T1 - Integration Aspects of Plasmonic TiN-based Nano-Hole-Arrays on Ge Photodetectorsin a 200mm Wafer CMOS Compatible Silicon Technology T2 - ECS Transactions N2 - In this work we present the progress in regard to the integration of a surface plasmon resonance refractive index sensor into a CMOS compatible 200 mm wafer silicon-based technology. Our approach pursues the combination of germanium photodetectors with metallic nanohole arrays. The paper is focused on the technology development to fabricate large area photodetectors based on a modern design concept. In a first iteration we achieved a leakage current density of 82 mA/cm2 at reverse bias of 0.5 V and a maximum optical responsivity of 0.103 A/W measured with TE polarized light at λ = 1310 nm and a reversed bias of 1 V. For the realization of nanohole arrays we used thin Titanium nitride (TiN) layers deposited by a sputtering process. We were able to produce very homogenous TiN layers with a thickness deviation of around 10 % and RMS of 1.413 nm for 150 nm thick TiN layers. KW - plasmonics KW - nanohole array KW - germanium detector Y1 - 2022 U6 - https://doi.org/10.1149/10904.0035ecst SN - 1938-5862 VL - 109 IS - 4 SP - 35 EP - 46 ER - TY - GEN A1 - Strobel, Carsten A1 - Alvarado Chavarin, Carlos A1 - Richter, Karola A1 - Knaut, Martin A1 - Reif, Johanna A1 - Völkel, Sandra A1 - Jahn, Andreas A1 - Albert, Matthias A1 - Wenger, Christian A1 - Kirchner, Robert A1 - Bartha, Johann Wolfgang A1 - Mikolajick, Thomas T1 - Novel Graphene Adjustable-Barrier Transistor with Ultra-High Current Gain T2 - ACS Applied Materials & Interfaces N2 - A graphene-based three terminal barristor device was proposed to overcome the low on/off ratios and insufficient current saturation of conventional graphene field effect transistors. In this study, we fabricated and analyzed a novel graphene-based transistor, which resembles the structure of the barristor but uses a different operating condition. This new device, termed graphene adjustable-barriers transistor (GABT), utilizes a semiconductor-based gate rather than a metal−insulator gate structure to modulate the device currents. The key feature of the device is the two graphene-semiconductor Schottky barriers with different heights that are controlled simultaneously by the gate voltage. Due to the asymmetry of the barriers, the drain current exceeds the gate current by several orders of magnitude. Thus, the GABT can be considered an amplifier with an alterable current gain. In this work, a silicon−graphene−germanium GABT with an ultra-high current gain (ID/IG up to 8 × 106) was fabricated, and the device functionality was demonstrated. Additionally, a capacitance model is applied to predict the theoretical device performance resulting in an on−off ratio above 106, a swing of 87 mV/dec, and a drivecurrent of about 1 × 106 A/cm2. KW - Graphene KW - Transistor Y1 - 2022 U6 - https://doi.org/10.1021/acsami.2c10634 SN - 1944-8244 SN - 1944-8252 VL - 14 IS - 34 SP - 39249 EP - 39254 ER -