This research group deals with the development of functional materials for microelectronics. Memristive devices have a variable resistance-based memory function. This type of component is of particular interest as a switchable element for non-volatile RRAM memories, but also for the area of analog neural circuit technology. The research area of plasmonics deals with the properties of electromagnetic near fields in CMOS-compatible nanostructures, which are able to locally increase electromagnetic fields and thus open up a broad field of application.
In neuronal circuits, the memristive devices open up the possibility of overcoming the currently existing hurdles of digital data processing in the area of cognitive tasks, such as pattern recognition. The focus of the research strategy is the development of memristive devices for future electronic circuits with a strong focus on biological systems.
While light is used in spectroscopy to understand the properties of materials, plasmonic effects can be used to develop nanostructures in order to significantly increase electromagnetic fields in small areas. With this plasmonic approach, a new type of sensor system can be developed that enables a fast and reliable method to identify biological species.
Main targets
- development of plasmonic devices for near-field sensors
- development of resistive devices for non-volatile memories
- development of electronic synapses for neural networks
Research topics
- development of memristive arrays for edge computing and neuromorphic circuits
- electrical characterization, simulation and modeling of memristive devices
- simulation and characterization of cell manipulation on lab-on-chip systems
- development and characterization of dielectric sensors
- development and integration of plasmonic sensors for environmental monitoring and biosensors
Research result
Script list Publications
(1) Variability in HfO2-based Memristors Described with a New Bidimensional Statistical Technique
C. Acal, D. Maldonado, A. Cantudo, M.B. González, F. Jiménez-Molinos, F. Campabadal, J.B. Roldán
Nanoscale 16(22), 10812 (2024)
DOI: 10.1039/D4NR01237B
A new statistical analysis is presented to assess cycle-to-cycle variability in resistive memories. This method employs two-dimensional (2D) distributions of parameters to analyse both set and reset voltages and currents, coupled with a 2D coefficient of variation (CV). This 2D methodology significantly enhances the analysis, providing a more thorough and comprehensive understanding of the data compared to conventional one-dimensional methods. Resistive switching (RS) data from two different technologies based on hafnium oxide are used in the variability study. The 2D CV allows a more compact assessment of technology suitability for applications such as non-volatile memories, neuromorphic computing and random number generation circuits.
(2) Low-Power Consumption IGZO Memristor-Based Gas Sensor Embedded in an Internet of Things Monitoring System for Isopropanol Alcohol Gas
M. Chae, D. Lee, H.-D. Kim
Micromachines 15(1), 77 (2024)
DOI: 10.3390/mi15010077
Low-power-consumption gas sensors are crucial for diverse applications, including environmental monitoring and portable Internet of Things (IoT) systems. However, the desorption and adsorption characteristics of conventional metal oxide-based gas sensors require supplementary equipment, such as heaters, which is not optimal for low-power IoT monitoring systems. Memristor-based sensors (gasistors) have been investigated as innovative gas sensors owing to their advantages, including high response, low power consumption, and room-temperature (RT) operation. Based on IGZO, the proposed isopropanol alcohol (IPA) gas sensor demonstrates a detection speed of 105 s and a high response of 55.15 for 50 ppm of IPA gas at RT. Moreover, rapid recovery to the initial state was achievable in 50 μs using pulsed voltage and without gas purging. Finally, a low-power circuit module was integrated for wireless signal transmission and processing to ensure IoT compatibility. The stability of sensing results from gasistors based on IGZO has been demonstrated, even when integrated into IoT systems. This enables energy-efficient gas analysis and real-time monitoring at ~0.34 mW, supporting recovery via pulse bias. This research offers practical insights into IoT gas detection, presenting a wireless sensing system for sensitive, low-powered sensors.
(3) Fast Circuit Simulation of Memristive Crossbar Arrays with Bimodal Stochastic Synaptic Weights
N. Dersch, Ch. Roemer, E. Perez, Ch. Wenger, M. Schwarz, B. Iniguez, A. Kloes
Proc. IEEE Latin American Electron Devices Conference (LAEDC 2024), (2024)
DOI: 10.1109/LAEDC61552.2024.10555829, (KI-IoT)
(4) Characterization of Drop Cast as Strategy for the Biofunctionalization of Plasmonic Sensors Based on Highly Doped Ge-Based
E. Hardt, R. Varricchio, C.A. Chavarin, O. Skibitzki, A. di Masi, G. Capellini
Proc. iCampµs Cottbus Conference (iCCC 2024), 191 (2024)
(iCampus II)
(5) Selective Growth of GaP Crystals on CMOS-Compatible Si Nanotips Wafer by Gas Source Molecular Beam Epitaxy
N. Kafi, S. Kang, C. Golz, A. Rodrigues-Weisensee, L. Persichetti, D. Ryzhak, G. Capellini, D. Spirito, M. Schmidbauer, A. Kwasniewski, C. Netzel, O. Skibitzki, F. Hatami
Crystal Growth & Design 24(7), 2724 (2024)
DOI: 10.1021/acs.cgd.3c01337, (NHEQuanLEA)
Gallium phosphide (GaP) is a III–V semiconductor with remarkable optoelectronic properties, and it has almost the same lattice constant as silicon (Si). However, to date, the monolithic and large-scale integration of GaP devices with silicon remains challenging. In this study, we present a nanoheteroepitaxy approach using gas-source molecular-beam epitaxy for selective growth of GaP islands on Si nanotips, which were fabricated using complementary metal–oxide semiconductor (CMOS) technology on a 200 mm n-type Si(001) wafer. Our results show that GaP islands with sizes on the order of hundreds of nanometers can be successfully grown on CMOS-compatible wafers. These islands exhibit a zinc-blende phase and possess optoelectronic properties similar to those of a high-quality epitaxial GaP layer. This result marks a notable advancement in the seamless integration of GaP-based devices with high scalability into Si nanotechnology and integrated optoelectronics.
(6) Controlled Integration of InP Nanoislands with CMOS-Compatible Si using Nanoheteroepitaxy Approach
A. Kamath, D. Ryzhak, A. Rodrigues, N. Kafi, C. Golz, D. Spirito, O. Skibitzki, L. Persichetti, M. Schmidbauer, F. Hatami
Materials Science in Semiconductor Processing 182, 108585 (2024)
DOI: 10.1016/j.mssp.2024.108585, (NHEQuanLEA)
Indium phosphide (InP) nanoislands are grown on pre-patterned Silicon (001) nanotip substrate using gas-source molecular-beam epitaxy via nanoheteroepitaxy approach. The study explores the critical role of growth temperature in achieving selectivity, governed by diffusion length. Our study reveals that temperatures of about 480 °C and lower, lead to parasitic growth, while temperatures about 540 °C with an indium growth rate of about 0.7 Å.s−1 and phosphine flux of 4 sccm inhibit selective growth. The establishment of an optimal temperature window for selective InP growth is demonstrated for a temperature range of 490 °C to 530 °C. Comprehensive structural and optical analyses using atomic force microscopy, Raman spectroscopy, x-ray diffraction, and photoluminescence confirm a zincblende structure of indium phosphide with fully relaxed islands. These results demonstrate the capability to precisely tailor the position of InP nanoislands through a noncatalytic nanoheteroepitaxy approach, marking a crucial advancement in integrating InP nanoisland arrays on silicon devices.
(7) Rational Design and Development of Room Temperature Hydrogen Sensors Compatible with CMOS Technology: A Necessary Step for the Coming Renewable Hydrogen Economy
J. Kosto, R. Tschammer, C. Morales, K. Henkel, C.A. Chavarin, I. Costina, M. Ratzke, Ch. Wenger, I.A. Fischer, J.I. Flege
Proc. iCampus Confernce Cottbus (iCCC 2024), 182 (2024)
DOI: 10.5162/iCCC2024/P21
(8) Influence of Stop and Gate Voltage on Resistive Switching of 1T1R HfO2-based Memristors, a Modeling and Variability Analysis
D. Maldonado, A. Cantudo, K.D.S. Reddy, S. Pechmann, M. Uhlmann, Ch. Wenger, J.B. Roldán, E. Pérez
Materials Science in Semiconductor Processing 182, 108726 (2024)
DOI: 10.1016/j.mssp.2024.108726, (KI-IoT)
Memristive devices, particularly resistive random access memory (RRAM) cells based on hafnium oxide (HfO₂) dielectrics, exhibit promising characteristics for a wide range of applications. In spite of their potential, issues related to intrinsic variability and the need for precise simulation tools and modeling methods remain a medium-term hurdle. This study addresses these challenges by investigating the resistive switching (RS) behavior of different 1T1R HfO₂-based memristors under various experimental conditions. Through a comprehensive experimental analysis, we extract RS parameters using different numerical techniques to understand the cycle-to-cycle (C2C) and device-to-device (D2D) variability. Additionally, we employ advanced simulation methodologies, including circuit breaker-based 3D simulation, to shed light on our experimental findings and provide a theoretical framework to disentangle the switching phenomena. Our results offer valuable insights into the RS mechanisms and variability, contributing to the improvement of robust parameter extraction methods, which are essential for the industrial application of memristive devices.
(9) Blooming and Pruning: Learning from Mistakes with Memristive Synapses
K. Nikiruy, E. Perez, A. Baroni, K.D.S. Reddy, S. Pechmann, Ch. Wenger, M. Ziegler
Scientific Reports 14, 7802 (2024)
DOI: 10.1038/s41598-024-57660-4, (KI-IoT)
Blooming and pruning is one of the most important developmental mechanisms of the biological brain in the first years of life, enabling it to adapt its network structure to the demands of the environment. The mechanism is thought to be fundamental for the development of cognitive skills. Inspired by this, Chialvo and Bak proposed in 1999 a learning scheme that learns from mistakes by eliminating from the initial surplus of synaptic connections those that lead to an undesirable outcome. Here, this idea is implemented in a neuromorphic circuit scheme using CMOS integrated HfO2-based memristive devices. The implemented two-layer neural network learns in a self-organized manner without positive reinforcement and exploits the inherent variability of the memristive devices. A combined experimental and simulation-based parameter study is presented to find the relevant system and device parameters leading to a compact and robust memristive neuromorphic circuit that can handle association tasks.
(10) A Current Mirror Based Read Circuit Design with Multi-Level Capability for Resistive Switching Devices
S. Pechmann, E. Perez, Ch. Wenger, A. Hagelauer
Proc. International Conference on Electronics, Information, and Communication (ICEIC 2024), (2024)
DOI: 10.1109/ICEIC61013.2024.10457188, (KI-IoT)
(11) Stochastic Resonance in 2D Materials Based Memristors
J.B. Roldán, A. Cantudo, J.J. Torres, D. Maldonado, Y. Shen, W. Zheng, Y. Yuan, M. Lanza
Nature Nanotechnology 8, 7 (2024)
DOI: 10.1038/s41699-024-00444-1, (KI-IoT)
Stochastic resonance is an essential phenomenon in neurobiology, it is connected to the constructive role of noise in the signals that take place in neuronal tissues, facilitating information communication, memory, etc. Memristive devices are known to be the cornerstone of hardware neuromorphic applications since they correctly mimic biological synapses in many different facets, such as short/long-term plasticity, spike-timing-dependent plasticity, pair-pulse facilitation, etc. Different types of neural networks can be built with circuit architectures based on memristive devices (mostly spiking neural networks and artificial neural networks). In this context, stochastic resonance is a critical issue to analyze in the memristive devices that will allow the fabrication of neuromorphic circuits. We do so here with h-BN based memristive devices from different perspectives. It is found that the devices we have fabricated and measured clearly show stochastic resonance behaviour. Consequently, neuromorphic applications can be developed to account for this effect, that describes a key issue in neurobiology with strong computational implications.
(12) Thermal Compact Modeling and Resistive Switching Analysis in Titanium Oxide-Based Memristors
J.B. Roldán, A. Cantudo, D. Maldonado, C. Aguilera-Pedregosa, E. Moreno, T. Swoboda, F. Jimenez-Molinos, Y. Yuan, K. Zhu, M. Lanza, M.M. Rojo
ACS Applied Electronic Materials 6(2), 1424 (2024)
DOI: 10.1021/acsaelm.3c01727, (KI-IoT)
Resistive switching devices based on the Au/Ti/TiO2/Au stack were developed. In addition to standard electrical characterization by means of I–V curves, scanning thermal microscopy was employed to localize the hot spots on the top device surface (linked to conductive nanofilaments, CNFs) and perform in-operando tracking of temperature in such spots. In this way, electrical and thermal responses can be simultaneously recorded and related to each other. In a complementary way, a model for device simulation (based on COMSOL Multiphysics) was implemented in order to link the measured temperature to simulated device temperature maps. The data obtained were employed to calculate the thermal resistance to be used in compact models, such as the Stanford model, for circuit simulation. The thermal resistance extraction technique presented in this work is based on electrical and thermal measurements instead of being indirectly supported by a single fitting of the electrical response (using just I–V curves), as usual. Besides, the set and reset voltages were calculated from the complete I–V curve resistive switching series through different automatic numerical methods to assess the device variability. The series resistance was also obtained from experimental measurements, whose value is also incorporated into a compact model enhanced version.
(13) Selective Epitaxy of Germanium on Silicon for the Fabrication of CMOS Compatible Short-Wavelength Infrared Photodetectors
D. Ryzhak, A.A. Corley-Wiciak, P. Steglich, Y. Yamamoto, J. Frigerio, R. Giani, A. De Iacovo, D. Spirito, G. Capellini
Materials Science in Semiconductor Processing 176, 108308 (2024)
DOI: 10.1016/j.mssp.2024.108308, (VISIR2)
Here we present the selective epitaxial growth of Ge on Si using reduced pressure chemical vapor deposition on SiO2/Si solid masks realized on 200 mm Si wafers, aiming at manufacturing integrated visible/short-wavelength infrared photodetectors. By a suitable choice of the reactants and of the process conditions, we demonstrated highly selective and pattern-independent growth of Ge microstructure featuring high crystalline quality. The Ge “patches” show a distinct faceting, with a flat top (001) facet and low energy facets such as e.g. {113} and {103} at their sidewalls, independently on their size. Interdiffusion of Si in to the Ge microcrystals is limited to an extension of ∼20 nm from the heterointerface. The Ge patches resulted to be plastically relaxed with threading dislocation density values better or on par than those observed in continuous two-dimensional Ge/Si epilayer in the low 107 cm−2 range. A residual tensile strain was observed for patches with size >10 μm, due to elastic thermal strain accumulation, as confirmed by μ-Raman spectroscopy and μ-photoluminescence characterization. Polarization-dependent Raman mapping highlights the strain distribution associated to the tridimensional shape. On this material, Ge photodiodes were fabricated and characterized, showing promising optoelectronic performances.
(14) On the Asymmetry of Resistive Switching Transitions
G. Vinuesa, H. Garcia, E. Perez, Ch. Wenger, I. Iniguez-de-la-Torre, T. Gonzalez, S. Duenas, H. Castan,
Electronics (MDPI) 13(13), 2639 (2024)
DOI: 10.3390/electronics13132639, (KI-IoT)
In this study, the resistive switching phenomena in TiN/Ti/HfO2/Ti metal–insulator–metal stacks is investigated, mainly focusing on the analysis of set and reset transitions. The electrical measurements in a wide temperature range reveal that the switching transitions require less voltage (and thus, less energy) as temperature rises, with the reset process being much more temperature sensitive. The main conduction mechanism in both resistance states is Space-charge-limited Conduction, but the high conductivity state also shows Schottky emission, explaining its temperature dependence. Moreover, the temporal evolution of these transitions reveals clear differences between them, as their current transient response is completely different. While the set is sudden, the reset process development is clearly non-linear, closely resembling a sigmoid function. This asymmetry between switching processes is of extreme importance in the manipulation and control of the multi-level characteristics and has clear implications in the possible applications of resistive switching devices in neuromorphic computing.
C. Acal, D. Maldonado, A. Cantudo, M.B. González, F. Jiménez-Molinos, F. Campabadal, J.B. Roldán
Nanoscale 16(22), 10812 (2024)
DOI: 10.1039/D4NR01237B
A new statistical analysis is presented to assess cycle-to-cycle variability in resistive memories. This method employs two-dimensional (2D) distributions of parameters to analyse both set and reset voltages and currents, coupled with a 2D coefficient of variation (CV). This 2D methodology significantly enhances the analysis, providing a more thorough and comprehensive understanding of the data compared to conventional one-dimensional methods. Resistive switching (RS) data from two different technologies based on hafnium oxide are used in the variability study. The 2D CV allows a more compact assessment of technology suitability for applications such as non-volatile memories, neuromorphic computing and random number generation circuits.
(2) Low-Power Consumption IGZO Memristor-Based Gas Sensor Embedded in an Internet of Things Monitoring System for Isopropanol Alcohol Gas
M. Chae, D. Lee, H.-D. Kim
Micromachines 15(1), 77 (2024)
DOI: 10.3390/mi15010077
Low-power-consumption gas sensors are crucial for diverse applications, including environmental monitoring and portable Internet of Things (IoT) systems. However, the desorption and adsorption characteristics of conventional metal oxide-based gas sensors require supplementary equipment, such as heaters, which is not optimal for low-power IoT monitoring systems. Memristor-based sensors (gasistors) have been investigated as innovative gas sensors owing to their advantages, including high response, low power consumption, and room-temperature (RT) operation. Based on IGZO, the proposed isopropanol alcohol (IPA) gas sensor demonstrates a detection speed of 105 s and a high response of 55.15 for 50 ppm of IPA gas at RT. Moreover, rapid recovery to the initial state was achievable in 50 μs using pulsed voltage and without gas purging. Finally, a low-power circuit module was integrated for wireless signal transmission and processing to ensure IoT compatibility. The stability of sensing results from gasistors based on IGZO has been demonstrated, even when integrated into IoT systems. This enables energy-efficient gas analysis and real-time monitoring at ~0.34 mW, supporting recovery via pulse bias. This research offers practical insights into IoT gas detection, presenting a wireless sensing system for sensitive, low-powered sensors.
(3) Fast Circuit Simulation of Memristive Crossbar Arrays with Bimodal Stochastic Synaptic Weights
N. Dersch, Ch. Roemer, E. Perez, Ch. Wenger, M. Schwarz, B. Iniguez, A. Kloes
Proc. IEEE Latin American Electron Devices Conference (LAEDC 2024), (2024)
DOI: 10.1109/LAEDC61552.2024.10555829, (KI-IoT)
(4) Characterization of Drop Cast as Strategy for the Biofunctionalization of Plasmonic Sensors Based on Highly Doped Ge-Based
E. Hardt, R. Varricchio, C.A. Chavarin, O. Skibitzki, A. di Masi, G. Capellini
Proc. iCampµs Cottbus Conference (iCCC 2024), 191 (2024)
(iCampus II)
(5) Selective Growth of GaP Crystals on CMOS-Compatible Si Nanotips Wafer by Gas Source Molecular Beam Epitaxy
N. Kafi, S. Kang, C. Golz, A. Rodrigues-Weisensee, L. Persichetti, D. Ryzhak, G. Capellini, D. Spirito, M. Schmidbauer, A. Kwasniewski, C. Netzel, O. Skibitzki, F. Hatami
Crystal Growth & Design 24(7), 2724 (2024)
DOI: 10.1021/acs.cgd.3c01337, (NHEQuanLEA)
Gallium phosphide (GaP) is a III–V semiconductor with remarkable optoelectronic properties, and it has almost the same lattice constant as silicon (Si). However, to date, the monolithic and large-scale integration of GaP devices with silicon remains challenging. In this study, we present a nanoheteroepitaxy approach using gas-source molecular-beam epitaxy for selective growth of GaP islands on Si nanotips, which were fabricated using complementary metal–oxide semiconductor (CMOS) technology on a 200 mm n-type Si(001) wafer. Our results show that GaP islands with sizes on the order of hundreds of nanometers can be successfully grown on CMOS-compatible wafers. These islands exhibit a zinc-blende phase and possess optoelectronic properties similar to those of a high-quality epitaxial GaP layer. This result marks a notable advancement in the seamless integration of GaP-based devices with high scalability into Si nanotechnology and integrated optoelectronics.
(6) Controlled Integration of InP Nanoislands with CMOS-Compatible Si using Nanoheteroepitaxy Approach
A. Kamath, D. Ryzhak, A. Rodrigues, N. Kafi, C. Golz, D. Spirito, O. Skibitzki, L. Persichetti, M. Schmidbauer, F. Hatami
Materials Science in Semiconductor Processing 182, 108585 (2024)
DOI: 10.1016/j.mssp.2024.108585, (NHEQuanLEA)
Indium phosphide (InP) nanoislands are grown on pre-patterned Silicon (001) nanotip substrate using gas-source molecular-beam epitaxy via nanoheteroepitaxy approach. The study explores the critical role of growth temperature in achieving selectivity, governed by diffusion length. Our study reveals that temperatures of about 480 °C and lower, lead to parasitic growth, while temperatures about 540 °C with an indium growth rate of about 0.7 Å.s−1 and phosphine flux of 4 sccm inhibit selective growth. The establishment of an optimal temperature window for selective InP growth is demonstrated for a temperature range of 490 °C to 530 °C. Comprehensive structural and optical analyses using atomic force microscopy, Raman spectroscopy, x-ray diffraction, and photoluminescence confirm a zincblende structure of indium phosphide with fully relaxed islands. These results demonstrate the capability to precisely tailor the position of InP nanoislands through a noncatalytic nanoheteroepitaxy approach, marking a crucial advancement in integrating InP nanoisland arrays on silicon devices.
(7) Rational Design and Development of Room Temperature Hydrogen Sensors Compatible with CMOS Technology: A Necessary Step for the Coming Renewable Hydrogen Economy
J. Kosto, R. Tschammer, C. Morales, K. Henkel, C.A. Chavarin, I. Costina, M. Ratzke, Ch. Wenger, I.A. Fischer, J.I. Flege
Proc. iCampus Confernce Cottbus (iCCC 2024), 182 (2024)
DOI: 10.5162/iCCC2024/P21
(8) Influence of Stop and Gate Voltage on Resistive Switching of 1T1R HfO2-based Memristors, a Modeling and Variability Analysis
D. Maldonado, A. Cantudo, K.D.S. Reddy, S. Pechmann, M. Uhlmann, Ch. Wenger, J.B. Roldán, E. Pérez
Materials Science in Semiconductor Processing 182, 108726 (2024)
DOI: 10.1016/j.mssp.2024.108726, (KI-IoT)
Memristive devices, particularly resistive random access memory (RRAM) cells based on hafnium oxide (HfO₂) dielectrics, exhibit promising characteristics for a wide range of applications. In spite of their potential, issues related to intrinsic variability and the need for precise simulation tools and modeling methods remain a medium-term hurdle. This study addresses these challenges by investigating the resistive switching (RS) behavior of different 1T1R HfO₂-based memristors under various experimental conditions. Through a comprehensive experimental analysis, we extract RS parameters using different numerical techniques to understand the cycle-to-cycle (C2C) and device-to-device (D2D) variability. Additionally, we employ advanced simulation methodologies, including circuit breaker-based 3D simulation, to shed light on our experimental findings and provide a theoretical framework to disentangle the switching phenomena. Our results offer valuable insights into the RS mechanisms and variability, contributing to the improvement of robust parameter extraction methods, which are essential for the industrial application of memristive devices.
(9) Blooming and Pruning: Learning from Mistakes with Memristive Synapses
K. Nikiruy, E. Perez, A. Baroni, K.D.S. Reddy, S. Pechmann, Ch. Wenger, M. Ziegler
Scientific Reports 14, 7802 (2024)
DOI: 10.1038/s41598-024-57660-4, (KI-IoT)
Blooming and pruning is one of the most important developmental mechanisms of the biological brain in the first years of life, enabling it to adapt its network structure to the demands of the environment. The mechanism is thought to be fundamental for the development of cognitive skills. Inspired by this, Chialvo and Bak proposed in 1999 a learning scheme that learns from mistakes by eliminating from the initial surplus of synaptic connections those that lead to an undesirable outcome. Here, this idea is implemented in a neuromorphic circuit scheme using CMOS integrated HfO2-based memristive devices. The implemented two-layer neural network learns in a self-organized manner without positive reinforcement and exploits the inherent variability of the memristive devices. A combined experimental and simulation-based parameter study is presented to find the relevant system and device parameters leading to a compact and robust memristive neuromorphic circuit that can handle association tasks.
(10) A Current Mirror Based Read Circuit Design with Multi-Level Capability for Resistive Switching Devices
S. Pechmann, E. Perez, Ch. Wenger, A. Hagelauer
Proc. International Conference on Electronics, Information, and Communication (ICEIC 2024), (2024)
DOI: 10.1109/ICEIC61013.2024.10457188, (KI-IoT)
(11) Stochastic Resonance in 2D Materials Based Memristors
J.B. Roldán, A. Cantudo, J.J. Torres, D. Maldonado, Y. Shen, W. Zheng, Y. Yuan, M. Lanza
Nature Nanotechnology 8, 7 (2024)
DOI: 10.1038/s41699-024-00444-1, (KI-IoT)
Stochastic resonance is an essential phenomenon in neurobiology, it is connected to the constructive role of noise in the signals that take place in neuronal tissues, facilitating information communication, memory, etc. Memristive devices are known to be the cornerstone of hardware neuromorphic applications since they correctly mimic biological synapses in many different facets, such as short/long-term plasticity, spike-timing-dependent plasticity, pair-pulse facilitation, etc. Different types of neural networks can be built with circuit architectures based on memristive devices (mostly spiking neural networks and artificial neural networks). In this context, stochastic resonance is a critical issue to analyze in the memristive devices that will allow the fabrication of neuromorphic circuits. We do so here with h-BN based memristive devices from different perspectives. It is found that the devices we have fabricated and measured clearly show stochastic resonance behaviour. Consequently, neuromorphic applications can be developed to account for this effect, that describes a key issue in neurobiology with strong computational implications.
(12) Thermal Compact Modeling and Resistive Switching Analysis in Titanium Oxide-Based Memristors
J.B. Roldán, A. Cantudo, D. Maldonado, C. Aguilera-Pedregosa, E. Moreno, T. Swoboda, F. Jimenez-Molinos, Y. Yuan, K. Zhu, M. Lanza, M.M. Rojo
ACS Applied Electronic Materials 6(2), 1424 (2024)
DOI: 10.1021/acsaelm.3c01727, (KI-IoT)
Resistive switching devices based on the Au/Ti/TiO2/Au stack were developed. In addition to standard electrical characterization by means of I–V curves, scanning thermal microscopy was employed to localize the hot spots on the top device surface (linked to conductive nanofilaments, CNFs) and perform in-operando tracking of temperature in such spots. In this way, electrical and thermal responses can be simultaneously recorded and related to each other. In a complementary way, a model for device simulation (based on COMSOL Multiphysics) was implemented in order to link the measured temperature to simulated device temperature maps. The data obtained were employed to calculate the thermal resistance to be used in compact models, such as the Stanford model, for circuit simulation. The thermal resistance extraction technique presented in this work is based on electrical and thermal measurements instead of being indirectly supported by a single fitting of the electrical response (using just I–V curves), as usual. Besides, the set and reset voltages were calculated from the complete I–V curve resistive switching series through different automatic numerical methods to assess the device variability. The series resistance was also obtained from experimental measurements, whose value is also incorporated into a compact model enhanced version.
(13) Selective Epitaxy of Germanium on Silicon for the Fabrication of CMOS Compatible Short-Wavelength Infrared Photodetectors
D. Ryzhak, A.A. Corley-Wiciak, P. Steglich, Y. Yamamoto, J. Frigerio, R. Giani, A. De Iacovo, D. Spirito, G. Capellini
Materials Science in Semiconductor Processing 176, 108308 (2024)
DOI: 10.1016/j.mssp.2024.108308, (VISIR2)
Here we present the selective epitaxial growth of Ge on Si using reduced pressure chemical vapor deposition on SiO2/Si solid masks realized on 200 mm Si wafers, aiming at manufacturing integrated visible/short-wavelength infrared photodetectors. By a suitable choice of the reactants and of the process conditions, we demonstrated highly selective and pattern-independent growth of Ge microstructure featuring high crystalline quality. The Ge “patches” show a distinct faceting, with a flat top (001) facet and low energy facets such as e.g. {113} and {103} at their sidewalls, independently on their size. Interdiffusion of Si in to the Ge microcrystals is limited to an extension of ∼20 nm from the heterointerface. The Ge patches resulted to be plastically relaxed with threading dislocation density values better or on par than those observed in continuous two-dimensional Ge/Si epilayer in the low 107 cm−2 range. A residual tensile strain was observed for patches with size >10 μm, due to elastic thermal strain accumulation, as confirmed by μ-Raman spectroscopy and μ-photoluminescence characterization. Polarization-dependent Raman mapping highlights the strain distribution associated to the tridimensional shape. On this material, Ge photodiodes were fabricated and characterized, showing promising optoelectronic performances.
(14) On the Asymmetry of Resistive Switching Transitions
G. Vinuesa, H. Garcia, E. Perez, Ch. Wenger, I. Iniguez-de-la-Torre, T. Gonzalez, S. Duenas, H. Castan,
Electronics (MDPI) 13(13), 2639 (2024)
DOI: 10.3390/electronics13132639, (KI-IoT)
In this study, the resistive switching phenomena in TiN/Ti/HfO2/Ti metal–insulator–metal stacks is investigated, mainly focusing on the analysis of set and reset transitions. The electrical measurements in a wide temperature range reveal that the switching transitions require less voltage (and thus, less energy) as temperature rises, with the reset process being much more temperature sensitive. The main conduction mechanism in both resistance states is Space-charge-limited Conduction, but the high conductivity state also shows Schottky emission, explaining its temperature dependence. Moreover, the temporal evolution of these transitions reveals clear differences between them, as their current transient response is completely different. While the set is sudden, the reset process development is clearly non-linear, closely resembling a sigmoid function. This asymmetry between switching processes is of extreme importance in the manipulation and control of the multi-level characteristics and has clear implications in the possible applications of resistive switching devices in neuromorphic computing.
