News


World's Top 2% Scientists 2023
World's Top 2% Scientists 2023

Two researchers from the Instituto de Microelectrónica de Sevilla (IMSE-CNM), Teresa Serrano Gotarredona and Bernabé Linares Barranco, maintain their inclusion in the new edition of the 'World's Top 2% Scientists' ranking of Stanford University (California).
November 21, 2023

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Electronic Eye System Vision
The electronic eye that imitates human vision

The Instituto de Microelectrónica de Sevilla (IMSE), dependent on the Higher Council for Scientific Research (CSIC) and the Universidad de Sevilla, has focused on the system that makes vision possible.
November 20, 2023

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Nanochip developed at IMSE
Spanish researchers develop a nanochip to protect electronic devices against cyberattacks

The chip, obtained by researchers from the IMSE (CSIC-University of Seville), allows to generate a unique digital key of the device that can be used to generate ephemeral cryptographic passwords of high security. The chip is the result of the SPIRS project.
October 9, 2023

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ESSCIRC ESSDERC Congress 2023
2023 European Researchers' Night

We invite you to discover the most human side of research through direct contact with the experts themselves. It is the European Night of Researchers , which we celebrate this year on September 29 for the twelfth consecutive year and at the same time in almost 400 European cities.
September 25, 2023

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ESSCIRC ESSDERC Congress 2023
Start of the QUBIP project

The kick-off meeting of the QUBIP project was held last September 12-13, 2023 in Torino (Italy). QUBIP is an EU-funded project (Horizon Europe programme - "Increased cybersecurity" cluster 3) that is coordinated by LINKS Foundation. In QUBIP, the IMSE participates leading the transition towards Post-Quantum Cryptography in the IoT pilot under the coordination of Dr. Piedad Brox.
September 25, 2023

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ESSCIRC ESSDERC Congress 2023
Historical Edition of the Main Microelectronics Congress in Europe

The ESSCIRC/ESSDERC congress was held in Lisbon from September 11 to 14. which has been jointly organized by researchers from the UNINOVA Institute & NOVA School of Science (Lisbon), ST Microelectronics and the Instituto de Microelectrónica de Sevilla, a joint center of the Universidad de Sevilla and the Consejo Superior de Investigaciones Científicas.
September 21, 2023

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PREVIOUS EVENTS & NEWS

New Director of the IMSE-CNM


IMSE researcher Teresa Serrano Gotarredona has been appointed as the new Director of the Instituto de Microelectrónica de Sevilla.

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Education at IMSE


- Doctoral Studies
- Master Studies
- Degree Studies
- Final Degree Projects
- Internships

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Recent publications


Experimental Demonstration of Coupled Differential Oscillator Networks for Versatile Applications
M. Jiménez, J. Núñez, J. Shamsi, B. Linares-Barranco and M.J. Avedillo
Journal Paper · Frontiers in Neuroscience, Neuromorphic Engineering, vol. 17, 2023
FRONTIERS    ISSN: 1662-453X
abstract      doi      

Oscillatory Neural Networks (ONNs) exhibit a high potential for energy-efficient computing. In ONNs, neurons are implemented with oscillators and synapses with resistive and/or capacitive coupling between pairs of oscillators. Computing is carried out on the basis of the rich, complex, nonlinear synchronization dynamics of a system of coupled oscillators. The exploited synchronization phenomena in ONNs are an example of fully parallel collective computing.A fast system´s convergence to stable states, which correspond to the desired processed information, enables an energy-efficient solution if small area and low-power oscillators are used, specifically, when they are built on the basis of the hysteresis exhibited by phase-transition materials such as VO2. In recent years, there have been numerous studies on ONNs using VO2. Most of them report simulation results. Although in some cases experimental results are also shown, they don´t implement the design techniques that other works on electrical simulations report that allow to improve the behavior of the ONNs.Experimental validation of these approaches is necessary. Therefore, in this work, we describe an ONN realized in a commercial CMOS technology in which the oscillators are built using a circuit that we have developed to emulate the VO2 device. The purpose is to be able to study in depth the synchronization dynamics of relaxation oscillators similar to those that can be performed with VO2 devices. The fabricated circuit is very flexible. It allows programming the synapses to implement different ONNs, calibrating the frequency of the oscillators or controlling their initialization. It uses differential oscillators and resistive synapses equivalent to the use of memristors. In this article, the designed and fabricated circuit is described in detail and experimental results are shown. Specifically, its satisfactory operation as an associative memory is demonstrated. The experiments carried out allow us to conclude that the ONN must be operated according to the type of computational task to be solved, and guidelines are extracted in this regard.

Learning Algorithms for Oscillatory Neural Networks as Associative Memory for Pattern Recognition
M. Jiménez, M.J. Avedillo, B. Linares-Barranco and J. Núñez
Journal Paper · Frontiers in Neuroscience, Neuromorphic Engineering, vol. 17, 2023
FRONTIERS    ISSN: 1662-453X
abstract      doi      

Alternative paradigms to the von Neumann computing scheme are currently arousing huge interest. Oscillatory neural networks (ONNs) using emerging phase-change materials like VO2 constitute an energy-efficient, massively parallel, brain-inspired, in-memory computing approach. The encoding of information in the phase pattern of frequency-locked, weakly coupled oscillators makes it possible to exploit their rich nonlinear dynamics and their synchronization phenomena for computing. A single fully connected ONN layer can implement an auto-associative memory comparable to that of a Hopfield network, hence Hebbian learning rule is the most widely adopted method for configuring ONNs for such applications, despite its well-known limitations. An extensive amount of literature is available about learning in Hopfield networks, with information regarding many different learning algorithms that perform better than the Hebbian rule. However, not all of these algorithms are useful for ONN training due to the constraints imposed by their physical implementation. This paper evaluates different learning methods with respect to their suitability for ONNs. It proposes a new approach, which is compared against previous works. The proposed method has been shown to produce competitive results in terms of pattern recognition accuracy with reduced precision in synaptic weights, and to be suitable for online learning.

 


Exploitation of Subharmonic Injection Locking for Solving Combinatorial Optimization Problems with Coupled Oscillators using VO2 based devices
J. Núñez, M.J. Avedillo and M. Jiménez
Conference · International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design SMACD 2023
abstract     

Abstract not available

Energy-efficient Brain-inspired Oscillatory Neural Networks using Phase-Transition Material
M. Jiménez, B. Linares-Barranco, M.J. Avedillo and J. Núñez
Conference · Workshop on Deep Learning meets Neuromorphic Hardware. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases ECML PKDD 2023
abstract     

Oscillatory Neural Network (ONN) is a promising neuromorphic computing approach which uses networks of frequency-locked coupled oscillators, and their inherent parallel synchronization to compute. Also, ONN can be im-plemented using phase-transition materials using nano-scale area, low voltage amplitude and reduced power consumption, being an efficient way to im-plement oscillator-based computing. In state-of-theart, ONN is built with a fully-connected architecture, with coupling configured depending on the tar-get application. Its most widespread use has been as associative memory, but recently it is gathering interest as a solver for non-deterministic polynomial time problem (NP-hard). This is performed on the basis of encoding the NP-problem in the Ising model, so ONN operates as an Ising machine. ONN state naturally evolves to minimum points in the Hamiltonian energy function re-sorting to its rich non-lineal dynamics, supposing a promising paradigm of fast, low-power, parallel computation.

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IMSE corporate video


What we do


Our main area of specialization is the design of CMOS analog and mixed-signal integrated circuits and their use in different application contexts such as wireless communications, data conversion, smart imagers & vision sensors, biomedical devices, cybersecurity, neuromorphic computing and space technologies.

The IMSE-CNM staff consists of approximately one hundred people, including scientists and support personnel. IMSE-CNM employees are involved in advancing scientific knowledge, designing high level scientific-technical solutions and in technology transfer.

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