Dr. Stefania Carapezzi, Post-Doctoral Researcher, EU H2020 NeurONN, CNRS Occitanie Est,  presents today at MRS 2022 Spring Virtual Meeting! Her talk is about simulation of dynamics of electrically coupled oscillators based on vanadium dioxide for low-power neuromorphic applications.
Title: Electrothermal Simulations of Synchronization Dynamics of Coupled Beyond-CMOS Vanadium Dioxide Oscillators for Neuromorphic Computing Applications.

Abstract: In our information-centric era, flooded by data, self-guided inference without human intervention is essential in vital fields such as intelligent healthcare or smart industry. However, machine-learning approaches based on algorithms fed by larger and larger amounts of data and longer and longer training routines can devour a prohibitively high amount of energy. This is in contrast with the current trend of energy saving for greener electronics and sustainable exploitation of Earth resources. Traditional computing machines rely on the von Neumann paradigm, where data storage and data processing are physically separated. This entails movement of data when analysis tasks are required, which is energetically costly. Thus, to reduce the power consumption of computing urges the design for beyond von Neumann computing approaches. In this respect, the human brain is a source of inspiration for smart solutions, being an example of how sophisticated computation may be accomplished with reduced power consumption. Neural systems can be represented as elemental units yielding periodic signals, interconnected into networks. The functions of such networks are intimately correlated to the emerging of collective behaviors, where oscillatory synchronization is one of the most important examples of collective dynamics. Thus, it is critical to assess the relationships between the features of the elemental units, the interaction strengths and the network structure to achieve synchronization, if any [1].
In this contribution, we propose the implementation of oscillatory neural networks (ONNs) with Beyond Complementary-Metal-Oxide-Semiconductor (CMOS) devices. We show experimental and simulation results of devices and oscillators based on vanadium dioxide (VO2). VO2 undergoes a transition from a high-resistive monoclinic crystal structure to a low-resistive tetragonal rutile-like one, triggered by temperature. However, a switch in resistance is observed as well in two-terminal devices when the VO2 channel is flown by current, in this case the Joule effect being thought to drive the transition. This resistive switching is pivotal for implementing highly compact and scalable oscillators. We use dedicated technology computer-aided design (TCAD [2]) 3D electrothermal simulations of VO2 oscillators [3, 4]. Finally, exploiting the mixed-mode approach [2], we combine electrothermal TCAD device simulations with SPICE circuit simulations to investigate the dynamics of coupled VO2 oscillators. It is worth noting that this is the first time that mixed-mode simulations of the circuit dynamics of interacting VO2 oscillators are shown, to the best of our knowledge. Such simulations are crucial for realistically associating the device’s behavior at the neural network, which is at the core of the ONN function as an analogue computing engine. Our findings give insights on the entangled thermal and electrical behavior of a single VO2 oscillator as well as the synchronization behavior of networks of oscillators and help provide guidelines for the successful implementation of ONN technology.

Acknowledgments. Authors wish to thank Dr. S. Karg, IBM Research Europe, Zurich, Switzerland, for providing the experimental data used for the calibration of the TCAD model and the useful discussions about the experimental devices. Authors also wish to thank Dr. A. Nejim and Dr. A. Plews, of Silvaco Europe Ltd., Cambridgeshire, United Kingdom, for providing the customized version of PCM model [2] used to simulate the VO2 material as well as for the useful discussions about the TCAD and mixed mode simulations.[1] A. Todri-Sanial et al., IEEE Trans. Neural Netw. Learn. Syst., 2021. DOI: 10.1109/TNNLS.2021.3107771[2] ”Victory Device User Manual”, version 1.19.1.C, Silvaco Inc[3] E. Corti et al., Front. Neurosci., vol. 15, 2021. DOI: 10.3389/fnins.2021.628254[4] S. Carapezzi et al., IEEE J. Emerg. Sel. Topics Circuits Syst. 11, 4, 2021. DOI: 10.1109/JETCAS.2021.3128756.

Presenter: Dr. Stefania Carapezzi, Post-Doctoral Researcher, LIRMM, Univ. of Montpellier, CNRS, Montpellier, France

MRS Symposium EQ11