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  <doc>
    <id>27016</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
    <edition/>
    <issue/>
    <volume>176</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-02-16</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Advanced Temperature Dependent Statistical Analysis of Forming Voltage Distributions for Three Different HfO2-Based RRAM Technologies</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Solid State Electronics</parentTitle>
    <identifier type="issn">0038-1101</identifier>
    <identifier type="issn">1879-2405</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="Artikelnummer">107961</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Eduardo</firstName>
      <lastName>Pérez</lastName>
    </author>
    <submitter>
      <firstName>Christian</firstName>
      <lastName>Wenger</lastName>
    </submitter>
    <author>
      <firstName>David</firstName>
      <lastName>Maldonado</lastName>
    </author>
    <author>
      <firstName>Christian</firstName>
      <lastName>Acal</lastName>
    </author>
    <author>
      <firstName>Juan Eloy</firstName>
      <lastName>Ruiz-Castro</lastName>
    </author>
    <author>
      <firstName>Ana María</firstName>
      <lastName>Aguilera</lastName>
    </author>
    <author>
      <firstName>Francisco</firstName>
      <lastName>Jimenez-Molinos</lastName>
    </author>
    <author>
      <firstName>Juan Bautista</firstName>
      <lastName>Roldan</lastName>
    </author>
    <author>
      <firstName>Christian</firstName>
      <lastName>Wenger</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>RRAM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>HfO2</value>
    </subject>
    <collection role="institutes" number="1521">FG Halbleitermaterialien</collection>
  </doc>
  <doc>
    <id>27018</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>14</pageNumber>
    <edition/>
    <issue>3</issue>
    <volume>10</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-02-16</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Study of Quantized Hardware Deep Neural Networks Based on Resistive Switching Devices, Conventional versus Convolutional Approaches</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Electronics (MDPI)</parentTitle>
    <identifier type="issn">2079-9292</identifier>
    <identifier type="doi">10.3390/electronics10030346</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="Artikelnummer">346</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Rocío</firstName>
      <lastName>Romero-Zaliz</lastName>
    </author>
    <submitter>
      <firstName>Christian</firstName>
      <lastName>Wenger</lastName>
    </submitter>
    <author>
      <firstName>Eduardo</firstName>
      <lastName>Pérez</lastName>
    </author>
    <author>
      <firstName>Francisco</firstName>
      <lastName>Jimenez-Molinos</lastName>
    </author>
    <author>
      <firstName>Christian</firstName>
      <lastName>Wenger</lastName>
    </author>
    <author>
      <firstName>Juan Bautista</firstName>
      <lastName>Roldan</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>RRAM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>resistive switching</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>neural network</value>
    </subject>
    <collection role="institutes" number="1521">FG Halbleitermaterialien</collection>
  </doc>
  <doc>
    <id>27031</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject_ref</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation>IEEE</contributingCorporation>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-02-16</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Behavioral modeling of multilevel HfO2-based memristors for neuromorphic circuit simulation</title>
    <abstract language="eng">An artificial neural network based on resistive switching memristors is implemented and simulated in LTspice. The influence of memristor variability and the reduction of the continuous range of synaptic weights into a discrete set of conductance levels is analyzed. To do so, a behavioral model is proposed for multilevel resistive switching memristors based on Al-doped HfO2 dielectrics, and it is implemented in a spice based circuit simulator. The model provides an accurate description of the conductance in the different conductive states in addition to describe the device-to-device variability</abstract>
    <parentTitle language="eng">XXXV Conference on Design of Circuits and Integrated Systems (DCIS), Segovia, Spain</parentTitle>
    <identifier type="doi">10.1109/DCIS51330.2020.9268652</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Antonio Javier</firstName>
      <lastName>Perez-Avila</lastName>
    </author>
    <submitter>
      <firstName>Christian</firstName>
      <lastName>Wenger</lastName>
    </submitter>
    <author>
      <firstName>Gerardo</firstName>
      <lastName>Gonzalez-Cordero</lastName>
    </author>
    <author>
      <firstName>Eduardo</firstName>
      <lastName>Pérez</lastName>
    </author>
    <author>
      <firstName>Emilio</firstName>
      <lastName>Perez-Bosch Quesada</lastName>
    </author>
    <author>
      <firstName>Mamathamba Kalishettyhalli</firstName>
      <lastName>Mahadevaiah</lastName>
    </author>
    <author>
      <firstName>Christian</firstName>
      <lastName>Wenger</lastName>
    </author>
    <author>
      <firstName>Juan Bautista</firstName>
      <lastName>Roldan</lastName>
    </author>
    <author>
      <firstName>Francisco</firstName>
      <lastName>Jimenez-Molinos</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>RRAM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multilevel switching</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>behavorial model</value>
    </subject>
    <collection role="institutes" number="1521">FG Halbleitermaterialien</collection>
  </doc>
  <doc>
    <id>27134</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>13</pageNumber>
    <edition/>
    <issue>6</issue>
    <volume>10</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-03-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Toward Reliable Compact Modeling of Multilevel 1T-1R RRAM Devices for Neuromorphic Systems</title>
    <abstract language="eng">In this work, three different RRAM compact models implemented in Verilog-A are analyzed and evaluated in order to reproduce the multilevel approach based on the switching capability of&#13;
experimental devices. These models are integrated in 1T-1R cells to control their analog behavior by means of the compliance current imposed by the NMOS select transistor. Four different resistance&#13;
levels are simulated and assessed with experimental verification to account for their multilevel capability. Further, an Artificial Neural Network study is carried out to evaluate in a real scenario the viability of the multilevel approach under study.</abstract>
    <parentTitle language="eng">Electronics (MDPI)</parentTitle>
    <identifier type="issn">2079-9292</identifier>
    <identifier type="doi">10.3390/electronics10060645</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="Artikelnummer">645</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Emilio</firstName>
      <lastName>Pérez-Bosch Quesada</lastName>
    </author>
    <submitter>
      <firstName>Christian</firstName>
      <lastName>Wenger</lastName>
    </submitter>
    <author>
      <firstName>Rocío</firstName>
      <lastName>Romero-Zaliz</lastName>
    </author>
    <author>
      <firstName>Eduardo</firstName>
      <lastName>Pérez</lastName>
    </author>
    <author>
      <firstName>Mamathamba Kalishettyhalli</firstName>
      <lastName>Mahadevaiah</lastName>
    </author>
    <author>
      <firstName>John</firstName>
      <lastName>Reuben</lastName>
    </author>
    <author>
      <firstName>Markus Andreas</firstName>
      <lastName>Schubert</lastName>
    </author>
    <author>
      <firstName>Francisco</firstName>
      <lastName>Jiménez-Molinos</lastName>
    </author>
    <author>
      <firstName>Juan Bautista</firstName>
      <lastName>Roldán</lastName>
    </author>
    <author>
      <firstName>Christian</firstName>
      <lastName>Wenger</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>RRAM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>multilevel switching</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>compact modeling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Verilog-A</value>
    </subject>
    <collection role="institutes" number="1521">FG Halbleitermaterialien</collection>
  </doc>
  <doc>
    <id>27376</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
    <edition/>
    <issue>9</issue>
    <volume>10</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-05-03</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Optimization of Multi-Level Operation in RRAM Arrays for In-Memory Computing</title>
    <abstract language="eng">Accomplishing multi-level programming in resistive random access memory (RRAM) arrays with truly discrete and linearly spaced conductive levels is crucial in order to implement synaptic weights in hardware-based neuromorphic systems. In this paper, we implemented this feature on 4-kbit 1T1R RRAM arrays by tuning the programming parameters of the multi-level incremental step pulse with verify algorithm (M-ISPVA). The optimized set of parameters was assessed by comparing its results with a non-optimized one. The optimized set of parameters proved to be an effective way to define non-overlapped conductive levels due to the strong reduction of the device-to-device variability as well as of the cycle-to-cycle variability, assessed by inter-levels switching tests and during 1k reset-set cycles. In order to evaluate this improvement in real scenarios, the experimental characteristics of the RRAM devices were captured by means of a behavioral model, which was used to simulate two different neuromorphic systems: an 8×8 vector-matrixmultiplication (VMM) accelerator and a 4-layer feedforward neural network for MNIST database recognition. The results clearly showed that the optimization of the programming parameters improved both the precision of VMM results as well as the recognition accuracy of the neural network in about 6% compared with the use of non-optimized parameters.</abstract>
    <parentTitle language="eng">Electronics (MDPI)</parentTitle>
    <identifier type="issn">2079-9292</identifier>
    <identifier type="doi">10.3390/electronics10091084</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="Artikelnummer">1084</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Eduardo</firstName>
      <lastName>Pérez</lastName>
    </author>
    <submitter>
      <firstName>Christian</firstName>
      <lastName>Wenger</lastName>
    </submitter>
    <author>
      <firstName>Antonio Javier</firstName>
      <lastName>Pérez-Ávila</lastName>
    </author>
    <author>
      <firstName>Rocío</firstName>
      <lastName>Romero-Zaliz</lastName>
    </author>
    <author>
      <firstName>Mamathamba Kalishettyhalli</firstName>
      <lastName>Mahadevaiah</lastName>
    </author>
    <author>
      <firstName>Emilio</firstName>
      <lastName>Pérez-Bosch Quesada</lastName>
    </author>
    <author>
      <firstName>Juan Bautista</firstName>
      <lastName>Roldan</lastName>
    </author>
    <author>
      <firstName>Francisco</firstName>
      <lastName>Jiménez-Molinos</lastName>
    </author>
    <author>
      <firstName>Christian</firstName>
      <lastName>Wenger</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>RRAM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multilevel switching</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>In-memory computing</value>
    </subject>
    <collection role="institutes" number="1521">FG Halbleitermaterialien</collection>
  </doc>
  <doc>
    <id>27551</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>5</pageNumber>
    <edition/>
    <issue/>
    <volume>185</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-06-07</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Influence of variability on the performance of HfO2 memristor-based convolutional neural networks</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Solid State Electronics</parentTitle>
    <identifier type="issn">0038-1101</identifier>
    <identifier type="doi">10.1016/j.sse.2021.108064</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="Artikelnummer">108064</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Rocío</firstName>
      <lastName>Romero-Zaliz</lastName>
    </author>
    <submitter>
      <firstName>Christian</firstName>
      <lastName>Wenger</lastName>
    </submitter>
    <author>
      <firstName>Eduardo</firstName>
      <lastName>Pérez</lastName>
    </author>
    <author>
      <firstName>Francisco</firstName>
      <lastName>Jimenez-Molinos</lastName>
    </author>
    <author>
      <firstName>Christian</firstName>
      <lastName>Wenger</lastName>
    </author>
    <author>
      <firstName>Juan Bautista</firstName>
      <lastName>Roldan</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>RRAM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>neural network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>HfO2</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>memristive switching</value>
    </subject>
    <collection role="institutes" number="1521">FG Halbleitermaterialien</collection>
  </doc>
  <doc>
    <id>28196</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>24</issue>
    <volume>10</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-12-17</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">An Analysis on the Architecture and the Size of Quantized Hardware Neural Networks Based on Memristors</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Electronics (MDPI)</parentTitle>
    <identifier type="issn">2079-9292</identifier>
    <identifier type="doi">10.3390/electronics10243141</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="Artikelnummer">3141</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Rocio</firstName>
      <lastName>Romero-Zaliz</lastName>
    </author>
    <submitter>
      <firstName>Christian</firstName>
      <lastName>Wenger</lastName>
    </submitter>
    <author>
      <firstName>Antonio</firstName>
      <lastName>Cantudo</lastName>
    </author>
    <author>
      <firstName>Eduardo</firstName>
      <lastName>Pérez</lastName>
    </author>
    <author>
      <firstName>Francisco</firstName>
      <lastName>Jimenez-Molinos</lastName>
    </author>
    <author>
      <firstName>Christian</firstName>
      <lastName>Wenger</lastName>
    </author>
    <author>
      <firstName>Juan Bautista</firstName>
      <lastName>Roldan</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>RRAM</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>memristive device</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>neural network</value>
    </subject>
    <collection role="institutes" number="1521">FG Halbleitermaterialien</collection>
  </doc>
  <doc>
    <id>33883</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <title language="eng">TiN/Ti/HfO2/TiN Memristive Devices for Neuromorphic Computing: From Synaptic Plasticity to Stochastic Resonance</title>
    <abstract language="eng">We characterize TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing. We analyze different features that allow the devices to mimic biological synapses and present the models to reproduce analytically some of the data measured. In particular, we have measured the spike timing dependent plasticity behavior in our devices and later on we have modeled it. The spike timing dependent plasticity model was implemented as the learning rule of a spiking neural network that was trained to recognize the MNIST dataset. Variability is implemented and its influence on the network recognition accuracy is considered accounting for the number of neurons in the network and the number of training epochs. Finally, stochastic resonance is studied as another synaptic feature.It is shown that this effect is important and greatly depends on the noise statistical characteristics.</abstract>
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    <author>
      <firstName>Mamathamba Kalishettyhalli</firstName>
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      <firstName>Francisco</firstName>
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    <title language="eng">A comparison of resistive switching parameters for memristive devices with HfO2 monolayers and Al2O3/HfO2 bilayers at the wafer scale</title>
    <abstract language="eng">Memristive devices integrated in 200 mm wafers&#13;
manufactured in 130 nm CMOS technology with two different&#13;
dielectrics, namely, a HfO2 monolayer and an Al2O3/HfO2 bilayer,&#13;
have been measured. The cycle-to-cycle (C2C) and device-todevice (D2D) variability have been analyzed at the wafer scale using different numerical methods to extract the set (Vset) and reset (Vreset) voltages. Some interesting differences between both technologies were found in terms of switching characteristics</abstract>
    <parentTitle language="eng">14th Spanish Conference on Electron Devices (CDE 2023), Valencia, Spain, 06-08 June 2023</parentTitle>
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      <firstName>Christian</firstName>
      <lastName>Wenger</lastName>
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    <author>
      <firstName>Juan Bautista</firstName>
      <lastName>Roldan</lastName>
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      <value>RRAM</value>
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    <title language="eng">Impact of the series resistance on switching characteristics of 1T1R HfO₂-based RRAM devices</title>
    <abstract language="eng">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.</abstract>
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