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- ItemEnhanced thermal stability of yttrium oxide-based RRAM devices with inhomogeneous Schottky-barrier(Melville, NY : American Inst. of Physics, 2020) Piros, Eszter; Petzold, Stefan; Zintler, Alexander; Kaiser, Nico; Vogel, Tobias; Eilhardt, Robert; Wenger, Christian; Molina-Luna, Leopoldo; Alff, LambertThis work addresses the thermal stability of bipolar resistive switching in yttrium oxide-based resistive random access memory revealed through the temperature dependence of the DC switching behavior. The operation voltages, current levels, and charge transport mechanisms are investigated at 25 °C, 85 °C, and 125 °C, and show overall good temperature immunity. The set and reset voltages, as well as the device resistance in both the high and low resistive states, are found to scale inversely with increasing temperatures. The Schottky-barrier height was observed to increase from approximately 1.02 eV at 25 °C to approximately 1.35 eV at 125 °C, an uncommon behavior explained by interface phenomena. © 2020 Author(s).
- ItemAC electrokinetic immobilization of organic dye molecules(Berlin [u.a.] : Springer, 2020) Laux, Eva-Maria; Wenger, Christian; Bier, Frank F.; Hölzel, RalphThe application of inhomogeneous AC electric fields for molecular immobilization is a very fast and simple method that does not require any adaptions to the molecule’s functional groups or charges. Here, the method is applied to a completely new category of molecules: small organic fluorescence dyes, whose dimensions amount to only 1 nm or even less. The presented setup and the electric field parameters used allow immobilization of dye molecules on the whole electrode surface as opposed to pure dielectrophoretic applications, where molecules are attracted only to regions of high electric field gradients, i.e., to the electrode tips and edges. In addition to dielectrophoresis and AC electrokinetic flow, molecular scale interactions and electrophoresis at short time scales are discussed as further mechanisms leading to migration and immobilization of the molecules. [Figure not available: see fulltext.] © 2020, The Author(s).
- ItemDielectrophoretic Immobilization of Yeast Cells Using CMOS Integrated Microfluidics(Basel : MDPI AG, 2020) Ettehad, Honeyeh Matbaechi; Soltani Zarrin, Pouya; Hölzel, Ralph; Wenger, ChristianThis paper presents a dielectrophoretic system for the immobilization and separation of live and dead cells. Dielectrophoresis (DEP) is a promising and efficient investigation technique for the development of novel lab-on-a-chip devices, which characterizes cells or particles based on their intrinsic and physical properties. Using this method, specific cells can be isolated from their medium carrier or the mixture of cell suspensions (e.g., separation of viable cells from non-viable cells). Main advantages of this method, which makes it favorable for disease (blood) analysis and diagnostic applications are, the preservation of the cell properties during measurements, label-free cell identification, and low set up cost. In this study, we validated the capability of complementary metal-oxide-semiconductor (CMOS) integrated microfluidic devices for the manipulation and characterization of live and dead yeast cells using dielectrophoretic forces. This approach successfully trapped live yeast cells and purified them from dead cells. Numerical simulations based on a two-layer model for yeast cells flowing in the channel were used to predict the trajectories of the cells with respect to their dielectric properties, varying excitation voltage, and frequency.
- ItemProgramming Pulse Width Assessment for Reliable and Low-Energy Endurance Performance in Al:HfO2-Based RRAM Arrays(Basel : MDPI AG, 2020) Pérez, Eduardo; Ossorio, Óscar González; Dueñas, Salvador; Castán, Helena; García, Héctor; Wenger, ChristianA crucial step in order to achieve fast and low-energy switching operations in resistive random access memory (RRAM) memories is the reduction of the programming pulse width. In this study, the incremental step pulse with verify algorithm (ISPVA) was implemented by using different pulse widths between 10 μ s and 50 ns and assessed on Al-doped HfO 2 4 kbit RRAM memory arrays. The switching stability was assessed by means of an endurance test of 1k cycles. Both conductive levels and voltages needed for switching showed a remarkable good behavior along 1k reset/set cycles regardless the programming pulse width implemented. Nevertheless, the distributions of voltages as well as the amount of energy required to carry out the switching operations were definitely affected by the value of the pulse width. In addition, the data retention was evaluated after the endurance analysis by annealing the RRAM devices at 150 °C along 100 h. Just an almost negligible increase on the rate of degradation of about 1 μ A at the end of the 100 h of annealing was reported between those samples programmed by employing a pulse width of 10 μ s and those employing 50 ns. Finally, an endurance performance of 200k cycles without any degradation was achieved on 128 RRAM devices by using programming pulses of 100 ns width.
- ItemIn-Vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Machine Learning Tools(New York, NY : IEEE, 2020) Zarrin, Pouya Soltani; Roeckendorf, Niels; Wenger, ChristianChronic 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 refiect the disease progression plays a pivotal role for the effective management of COPD. The accurate examination of respiratory tract fiuids like saliva is a promising approach for staging disease and predicting its upcoming exacerbations in a Point-of-Care (PoC) environment. However, the concurrent consideration of patients' demographic and medical parameters is necessary for achieving accurate outcomes. Therefore, Machine Learning (ML) tools can play an important role for analyzing patient data and providing comprehensive results for the recognition of COPD in a PoC setting. As a result, the objective of this research work was to implement ML tools on data acquired from characterizing saliva samples of COPD patients and healthy controls as well as their demographic information for PoC recognition of the disease. For this purpose, 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 and sensitivity of 91.25% and 100%, respectively, making it a promising model for COPD evaluation. Integration of this model on a neuromorphic chip, in the future, will enable the real-time assessment of COPD in PoC, with low cost, low energy consumption, and high patient privacy. In addition, constant monitoring of COPD in a near-patient setup will enable the better management of the disease exacerbations.
- ItemRole of Oxygen Defects in Conductive-Filament Formation in Y2O3-Based Analog RRAM Devices as Revealed by Fluctuation Spectroscopy(College Park, Md. [u.a.] : American Physical Society, 2020) Piros, Eszter; Lonsky, Martin; Petzold, Stefan; Zintler, Alexander; Sharath, S.U.; Vogel, Tobias; Kaiser, Nico; Eilhardt, Robert; Molina-Luna, Leopoldo; Wenger, Christian; Müller, Jens; Alff, LambertLow-frequency noise in Y2O3-based resistive random-access memory devices with analog switching is studied at intermediate resistive states and as a function of dc cycling. A universal 1/fα-type behavior is found, with a frequency exponent of α≈1.2 that is independent of the applied reset voltage or the device resistance and is attributed to the intrinsic abundance of oxygen vacancies unique to the structure of yttria. Remarkably, the noise magnitude in the high resistive state systematically decreases through dc training. This effect is attributed to the stabilization of the conductive filament via the consumption of oxygen vacancies, thus reducing the number of active fluctuators in the vicinity of the filament.
- ItemNeuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Zarrin, Pouya Soltani; Zahari, Finn; Mahadevaiah, Mamathamba K.; Perez, Eduardo; Kohlstedt, Hermann; Wenger, ChristianChronic 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. 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. © 2020, The Author(s).
- ItemAnalogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Zahari, Finn; Pérez, Eduardo; Mahadevaiah, Mamathamba Kalishettyhalli; Kohlstedt, Hermann; Wenger, Christian; Ziegler, MartinBiological neural networks outperform current computer technology in terms of power consumption and computing speed while performing associative tasks, such as pattern recognition. The analogue and massive parallel in-memory computing in biology differs strongly from conventional transistor electronics that rely on the von Neumann architecture. Therefore, novel bio-inspired computing architectures have been attracting a lot of attention in the field of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are employed to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology to neuromorphic systems. However, dealing with the inherent stochasticity of resistive switching can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two different RRAM technologies with different device variabilities are investigated in detail, and their potential applications in stochastic artificial neural networks (StochANNs) capable of solving MNIST pattern recognition tasks is examined. A mixed-signal implementation with hardware synapses and software neurons combined with numerical simulations shows that the proposed concept of stochastic computing is able to process analogue data with binary memory cells. © 2020, The Author(s).