@misc{SoltaniZarrinZahariMahadevaiahetal., author = {Soltani Zarrin, Pouya and Zahari, Finn and Mahadevaiah, Mamathamba Kalishettyhalli and P{\´e}rez, Eduardo and Kohlstedt, Hermann and Wenger, Christian}, title = {Neuromorphic on‑chip recognition of saliva samples of COPD and healthy controls using memristive devices}, series = {Scientific Reports}, volume = {10}, journal = {Scientific Reports}, issn = {2045-2322}, doi = {10.1038/s41598-020-76823-7}, abstract = {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.}, language = {en} } @misc{ZahariPerezMahadevaiahetal., author = {Zahari, Finn and P{\´e}rez, Eduardo and Mahadevaiah, Mamathamba Kalishettyhalli and Kohlstedt, Hermann and Wenger, Christian and Ziegler, Martin}, title = {Analogue pattern recognition with stochastic switching binary CMOS‑integrated memristive devices}, series = {Scientific Reports}, volume = {10}, journal = {Scientific Reports}, issn = {2045-2322}, doi = {10.1038/s41598-020-71334-x}, pages = {15}, abstract = {Biological neural networks outperform todays computer technology in terms of power consumption and computing speed when associative tasks, like pattern recognition, are to be solved. The analogue and massive parallel in-memory computing in biology differs strongly with conventional transistor electronics using the von Neumann architecture. Therefore, novel bio-inspired computing architectures are recently highly investigated in the area of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are used 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 in neuromorphic systems. However, dealing with the inherent stochasticity of the resistive switching effect 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 use in a stochastic artificial neural network (StochANN) to solve the MINST pattern recognition task is examined. A mixed-signal implementation with hardware synapses and software neurons as well as numerical simulations show the proposed concept of stochastic computing is able to handle analogue data with binary memory cells.}, language = {en} } @misc{PerezBoschQuesadaPerezMahadevaiahetal., author = {Perez-Bosch Quesada, Emilio and P{\´e}rez, Eduardo and Mahadevaiah, Mamathamba Kalishettyhalli and Wenger, Christian}, title = {Memristive-based in-memory computing: from device to large-scale CMOS integration}, series = {Neuromorphic Computing and Engineering}, volume = {1}, journal = {Neuromorphic Computing and Engineering}, number = {2}, issn = {2634-4386}, doi = {10.1088/2634-4386/ac2cd4}, pages = {8}, abstract = {With the rapid emergence of in-memory computing systems based on memristive technology, the integration of such memory devices in large-scale architectures is one of the main aspects to tackle. In this work we present a study of HfO2-based memristive devices for their integration in large-scale CMOS systems, namely 200 mm wafers. The DC characteristics of single metal-insulator-metal devices are analyzed taking under consideration device-to-device variabilities and switching properties. Furthermore, the distribution of the leakage current levels in the pristine state of the samples are analyzed and correlated to the amount of formingless memristors found among the measured devices. Finally, the obtained results are fitted into a physic-based compact model that enables their integration into larger-scale simulation environments.}, language = {en} } @misc{PerezMahadevaiahPerezBoschQuesadaetal., author = {P{\´e}rez, Eduardo and Mahadevaiah, Mamathamba Kalishettyhalli and Perez-Bosch Quesada, Emilio and Wenger, Christian}, title = {In-depth characterization of switching dynamics in amorphous HfO2 memristive arrays for the implementation of synaptic updating rules}, series = {Japanese Journal of Applied Physics}, volume = {61}, journal = {Japanese Journal of Applied Physics}, issn = {0021-4922}, doi = {10.35848/1347-4065/ac6a3b}, pages = {1 -- 7}, abstract = {Accomplishing truly analog conductance modulation in memristive arrays is crucial in order to implement the synaptic plasticity in hardware-based neuromorphic systems. In this paper, such a feature was addressed by exploiting the inherent stochasticity of switching dynamics in amorphous HfO2 technology. A thorough statistical analysis of experimental characteristics measured in 4 kbit arrays by using trains of identical depression/potentiation pulses with different voltage amplitudes and pulse widths provided the key to develop two different updating rules and to define their optimal programming parameters. The first rule is based on applying a specific number of identical pulses until the conductance value achieves the desired level. The second one utilized only one single pulse with a particular amplitude to achieve the targeted conductance level. In addition, all the results provided by the statistical analysis performed may play an important role in understanding better the switching behavior of this particular technology.}, language = {en} } @misc{PerezMaldonadoPerezBoschQuesadaetal., author = {P{\´e}rez, Eduardo and Maldonado, David and Perez-Bosch Quesada, Emilio and Mahadevaiah, Mamathamba Kalishettyhalli and Jimenez-Molinos, Francisco and Wenger, Christian}, title = {Parameter Extraction Methods for Assessing Device-to-Device and Cycle-to-Cycle Variability of Memristive Devices at Wafer Scale}, series = {IEEE Transactions on Electron Devices}, volume = {70}, journal = {IEEE Transactions on Electron Devices}, number = {1}, issn = {0018-9383}, doi = {10.1109/TED.2022.3224886}, pages = {360 -- 365}, abstract = {The stochastic nature of the resistive switching (RS) process in memristive devices makes device-to-device (DTD) and cycle-to-cycle (CTC) variabilities relevant magnitudes to be quantified and modeled. To accomplish this aim, robust and reliable parameter extraction methods must be employed. In this work, four different extraction methods were used at the production level (over all the 108 devices integrated on 200-mm wafers manufactured in the IHP 130-nm CMOS technology) in order to obtain the corresponding collection of forming, reset, and set switching voltages. The statistical analysis of the experimental data (mean and standard deviation (SD) values) was plotted by using heat maps, which provide a good summary of the whole data at a glance and, in addition, an easy manner to detect inhomogeneities in the fabrication process.}, language = {en} }