TY - GEN A1 - Pérez, 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 - Romero-Zaliz, Rocío A1 - Pérez, Eduardo A1 - Jimenez-Molinos, Francisco A1 - Wenger, Christian A1 - Roldan, Juan Bautista T1 - Study of Quantized Hardware Deep Neural Networks Based on Resistive Switching Devices, Conventional versus Convolutional Approaches T2 - Electronics (MDPI) N2 - A comprehensive analysis of two types of artificial neural networks (ANN) is performed to assess the influence of quantization on the synaptic weights. Conventional multilayer-perceptron (MLP) and convolutional neural networks (CNN) have been considered by changing their features in the training and inference contexts, such as number of levels in the quantization process, the number of hidden layers on the network topology, the number of neurons per hidden layer, the image databases, the number of convolutional layers, etc. A reference technology based on 1T1R structures with bipolar memristors including HfO2 dielectrics was employed, accounting for different multilevel schemes and the corresponding conductance quantization algorithms. The accuracy of the image recognition processes was studied in depth. This type of studies are essential prior to hardware implementation of neural networks. The obtained results support the use of CNNs for image domains. This is linked to the role played by convolutional layers at extracting image features and reducing the data complexity. In this case, the number of synaptic weights can be reduced in comparison to conventional MLPs. KW - RRAM KW - resistive switching KW - neural network Y1 - 2021 U6 - https://doi.org/10.3390/electronics10030346 SN - 2079-9292 VL - 10 IS - 3 ER - TY - GEN A1 - Soltani Zarrin, Pouya A1 - Zahari, Finn A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Pérez, 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 - Zanotti, Tommaso A1 - Puglisi, Francesco Maria A1 - Milo, Valerio A1 - Pérez, Eduardo A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Ossorio, Óscar G. A1 - Wenger, Christian A1 - Pavan, Paolo A1 - Olivo, Piero A1 - Ielmini, Daniele T1 - Reliability of Logic-in-Memory Circuits in Resistive Memory Arrays T2 - IEEE Transactions on Electron Devices N2 - Logic-in-memory (LiM) circuits based on resistive random access memory (RRAM) devices and the material implication logic are promising candidates for the development of low-power computing devices that could fulfill the growing demand of distributed computing systems. However, these circuits are affected by many reliability challenges that arise from device nonidealities (e.g., variability) and the characteristics of the employed circuit architecture. Thus, an accurate investigation of the variability at the array level is needed to evaluate the reliability and performance of such circuit architectures. In this work, we explore the reliability and performance of smart IMPLY (SIMPLY) (i.e., a recently proposed LiM architecture with improved reliability and performance) on two 4-kb RRAM arrays based on different resistive switching oxides integrated in the back end of line (BEOL) of the 0.25- μm BiCMOS process. We analyze the tradeoff between reliability and energy consumption of SIMPLY architecture by exploiting the results of an extensive array-level variability characterization of the two technologies. Finally, we study the worst case performance of a full adder implemented with the SIMPLY architecture and benchmark it on the analogous CMOS implementation. KW - RRAM KW - in-memory computing KW - HfO2 Y1 - 2020 U6 - https://doi.org/10.1109/TED.2020.3025271 SN - 0018-9383 SN - 1557-9646 VL - 67 IS - 11 SP - 4611 EP - 4615 ER - TY - GEN A1 - Zahari, Finn A1 - Pérez, Eduardo A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Kohlstedt, Hermann A1 - Wenger, Christian A1 - Ziegler, Martin T1 - Analogue pattern recognition with stochastic switching binary CMOS‑integrated memristive devices T2 - Scientific Reports N2 - 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. KW - RRAM KW - memristive device KW - neural network KW - HfO2 Y1 - 2020 U6 - https://doi.org/10.1038/s41598-020-71334-x SN - 2045-2322 VL - 10 ER - TY - GEN A1 - Pérez, Eduardo A1 - Ossorio, Óscar G. A1 - Dueñas, Salvador A1 - Castán, Helena A1 - García, Hector A1 - Wenger, Christian T1 - Programming Pulse Width Assessment for Reliable and Low-Energy Endurance Performance in Al:HfO2-Based RRAM Arrays T2 - Electronics (MDPI) N2 - A 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 KW - RRAM KW - Reliability Y1 - 2020 U6 - https://doi.org/10.3390/electronics9050864 SN - 2079-9292 VL - 9 IS - 5 ER - TY - GEN A1 - Milo, Valerio A1 - Anzalone, Francesco A1 - Zambelli, Cristian A1 - Pérez, 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 - Petryk, Dmytro A1 - Dyka, Zoya A1 - Pérez, Eduardo A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Kabin, Ievgen A1 - Wenger, Christian A1 - Langendörfer, Peter T1 - Evaluation of the Sensitivity of RRAM Cells to Optical Fault Injection Attacks T2 - EUROMICRO Conference on Digital System Design (DSD 2020), Special Session: Architecture and Hardware for Security Applications (AHSA) Y1 - 2021 SN - 978-1-7281-9535-3 U6 - https://doi.org/10.1109/DSD51259.2020.00047 SN - 978-1-7281-9536-0 ER - TY - GEN A1 - Romero-Zaliz, Rocío A1 - Pérez, Eduardo A1 - Jimenez-Molinos, Francisco A1 - Wenger, Christian A1 - Roldan, Juan Bautista T1 - Influence of variability on the performance of HfO2 memristor-based convolutional neural networks T2 - Solid State Electronics N2 - A study of convolutional neural networks (CNNs) was performed to analyze the influence of quantization and variability in the network synaptic weights. Different CNNs were considered accounting for the number of convolutional layers, size of the filters in the convolutional layer, number of neurons in the final network layers and different sets of quantization levels. The conductance levels of fabricated 1T1R structures based on HfO2 memristors were considered as reference for four or eight level quantization processes at the inference stage of the CNNs, which were previous trained with the MNIST dataset. We also included the variability of the experimental conductance levels that was found to be Gaussian distributed and was correspondingly modeled for the synaptic weight implementation. KW - RRAM KW - neural network KW - HfO2 KW - memristive switching Y1 - 2021 U6 - https://doi.org/10.1016/j.sse.2021.108064 SN - 0038-1101 VL - 185 ER - TY - GEN A1 - Ossorio, Óscar G. A1 - Vinuesa, Guillermo A1 - Garcia, Hector A1 - Sahelices, Benjamin A1 - Dueñas, Salvador A1 - Castán, Helena A1 - Pérez, Eduardo A1 - Mahadevaiah, Mamathamba Kalishettyhalli A1 - Wenger, Christian T1 - Performance Assessment of Amorphous HfO2-based RRAM Devices for Neuromorphic Applications T2 - ECS Transactions N2 - The use of thin layers of amorphous hafnium oxide has been shown to be suitable for the manufacture of Resistive Random-Access memories (RRAM). These memories are of great interest because of their simple structure and non-volatile character. They are particularly appealing as they are good candidates for substituting flash memories. In this work, the performance of the MIM structure that takes part of a 4 kbit memory array based on 1-transistor-1-resistance (1T1R) cells was studied in terms of control of intermediate states and cycle durability. DC and small signal experiments were carried out in order to fully characterize the devices, which presented excellent multilevel capabilities and resistive-switching behavior. KW - RRAM KW - resistive switching KW - HfO2 Y1 - 2021 U6 - https://doi.org/10.1149/10202.0029ecst SN - 1938-6737 SN - 1938-5862 VL - 102 IS - 2 SP - 29 EP - 35 ER - TY - GEN A1 - Baroni, Andrea A1 - Zambelli, Cristian A1 - Olivo, Piero A1 - Pérez, 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 - Romero-Zaliz, Rocio A1 - Cantudo, Antonio A1 - Pérez, Eduardo A1 - Jimenez-Molinos, Francisco A1 - Wenger, Christian A1 - Roldan, Juan Bautista T1 - An Analysis on the Architecture and the Size of Quantized Hardware Neural Networks Based on Memristors T2 - Electronics (MDPI) N2 - We have performed different simulation experiments in relation to hardware neural networks (NN) to analyze the role of the number of synapses for different NN architectures in the network accuracy, considering different datasets. A technology that stands upon 4-kbit 1T1R ReRAM arrays, where resistive switching devices based on HfO2 dielectrics are employed, is taken as a reference. In our study, fully dense (FdNN) and convolutional neural networks (CNN) were considered, where the NN size in terms of the number of synapses and of hidden layer neurons were varied. CNNs work better when the number of synapses to be used is limited. If quantized synaptic weights are included, we observed thatNNaccuracy decreases significantly as the number of synapses is reduced; in this respect, a trade-off between the number of synapses and the NN accuracy has to be achieved. Consequently, the CNN architecture must be carefully designed; in particular, it was noticed that different datasets need specific architectures according to their complexity to achieve good results. It was shown that due to the number of variables that can be changed in the optimization of a NN hardware implementation, a specific solution has to be worked in each case in terms of synaptic weight levels, NN architecture, etc. KW - RRAM KW - memristive device KW - neural network Y1 - 2021 U6 - https://doi.org/10.3390/electronics10243141 SN - 2079-9292 VL - 10 IS - 24 ER - TY - GEN A1 - Mannocci, Piergiulio A1 - Baroni, Andrea A1 - Melacarne, Enrico A1 - Zambelli, Cristian A1 - Olivo, Piero A1 - Pérez, Eduardo A1 - Wenger, Christian A1 - Ielmini, Daniele T1 - In-Memory Principal Component Analysis by Crosspoint Array of Rresistive Switching Memory T2 - IEEE Nanotechnology Magazine N2 - In Memory Computing (IMC) is one of the most promising candidates for data-intensive computing accelerators of machine learning (ML). A key ML algorithm for dimensionality reduction and classification is principal component analysis (PCA), which heavily relies on matrixvector multiplications (MVM) for which classic von Neumann architectures are not optimized. Here, we provide the experimental demonstration of a new IMCbased PCA algorithm based on power iteration and deflation executed in a 4-kbit array of resistive switching random-access memory (RRAM). The classification accuracy of the Wisconsin Breast Cancer data set reaches 95.43%, close to floatingpoint implementation. Our simulations indicate a 250× improvement in energy efficiency compared to commercial GPUs, thus supporting IMC for energy-efficient ML in modern data-intensive computing. KW - RRAM KW - Multilevel switching KW - neural network Y1 - 2022 U6 - https://doi.org/10.1109/MNANO.2022.3141515 SN - 1932-4510 VL - 16 IS - 2 SP - 4 EP - 13 ER -