Conventional von Neumann architecture faces many challenges in dealing with data-intensive artificial intelligence tasks efficiently due to huge amounts of data movement between physically separated data computing and storage units. Novel computing-in-memory (CIM) architecture implements data processing and storage in the same place, and thus can be much more energy-efficient than state-of-the-art von Neumann architecture. Compared with their counterparts, resistive random-access memory (RRAM)-based CIM systems could consume much less power and area when processing the same amount of data. This makes RRAM very attractive for both in-memory and neuromorphic computing applications.
In the field of machine learning, convolutional neural networks (CNN) are now widely used for artificial intelligence applications due to their significant performance. Nevertheless, for many tasks, machine learning requires large amounts of data and may be computationally very expensive and time consuming to train, with important issues (overfitting, exploding gradient and class imbalance). Among alternative brain-inspired computing paradigm, high-dimensional computing (HDC), based on random distributed representation, offers a promising way for learning tasks. Unlike conventional computing, HDC computes with (pseudo)-random hypervectors of D-dimension. This implies significant advantages: a simple algorithm with a well-defined set of arithmetic operations, with fast and single-pass learning that can benefit from a memory-centric architecture (highly energy-efficient and fast thanks to a high degree of parallelism).
Quantum computing is seen as a disruptive technology that could pave the way to the design of new computers able to solve problems out of reach of classical architectures. One of the challenges towards quantum computing is to provide solutions of electronics able to control and read the Qubits at cryogenic temperature.
Oscillators is a key function of cryogenic temperature that can be found in the qbit readout electronics or in the various reference frequency generators needed for high data-rate transmissions for example. The postdoc will consist in designing low phase, low power and low volume oscillator using an innovative optomechanical resonator under study in LETI and to study its performances at cryogenic temperature. The post doc will take advantage of already existing devices at LICA laboratory and on the experience of LGECA laboratory on the design of optomechanical conditioning circuits and on the characterization of cryogenic circuits.
The objective is to design a 2D matrix structure for quantum computing on silicon in order to consider structures of several hundred physical Qubits.
In particular the subject will be focused on:
- The functionality of the structure (Coulomb interaction, RF and quantum)
- Manufacturing constraints (simulation and realistic process constraint)
- The variability of the components (Taking into account the variability parameter and realistic defectivity)
- The constraints induced on the algorithms (error correction code)
- Scalability of the structure to thousands of physical Qubits
The candidate will work within a project of more than fifty people with expertise covering the design, fabrication, characterization and modeling of spin qubits as well as related disciplines (cryoelectronics, quantum algorithms, quantum error correction, …)
Because conventional downsizing based on the empirical Moore's law has reached its limitations, an alternative integration technology, such as three-dimensional integration (3DI) is becoming the mainstream. The 3rd generation of CMOS image sensor (CIS) stacks up to 3 die interconnected by hybrid bonding and High Density Through Silicon Vias (HD-TSVs). Devices and circuits good functioning and integrity have to be maintained in such an integration especially in the close neighborhood of TSVs. Thermal budget, copper pumping, thin wafer warpage can lead to electrical yield and reliability concerns and must be investigated.
The work consists in evaluating the impact of TSV processing and proximity on BEOL and FEOL performance and reliability. Acquired data sets will help to define design rules and in particular a potential Keep-Out Zone (KOZ) and calibrate a finite element model (FFM).
The goal of the postdoc is to evaluate the interest of smart imagers integrating processing in the focal plane in embedded vision systems for a localization function and to propose a complete embedded vision system integrating a smart imager and a host.
The study will focus on ego-localization applications, to realize, for example, a 3D localization function.
From an existing application chain, the post-doctoral fellow will be able to carry out an algorithmic study in order to optimize it to exploit the qualities of the intelligent imager.
Then he will be able to propose a partitioning between smart imager and host system, according to performance criteria.
An experiment using the RETINE smart imager as well as the IRIS host board could be conducted to validate the proposal.
To be able to increase and optimize wireless transmission systems based on a hybridization of technologies, it is strategic to be able to quickly evaluate the capabilities of these technologies and to adapt the associated architecture as best as possible. To this end, it is necessary to implement new approaches to global power management and optimization.
The work of this post-doctoral contract is at this level.
The first step will be to develop some new power consumption models of the RF transceivers building blocks (LNA, Mixer, Filter, PA, …). A modelization approach has already been tested and validated in the group. In the next step, it will be needed to link the performances of the overall wireless system to the building blocks characteristics. Lastly, the optimization will be applied thanks to an efficient solution. Lastly, the proposed approach will be validated in the optimisation of a multi-antenna millimeter wave wireless system. An evaluation methodology specific to 3D will also be put in place
The proposed topic is part of a CARNOT project aiming at developing a new generation of force sensors based on optomechanical transduction. These force sensors will be implemented in ultrafast AFM microscopes for imaging and force spectroscopy. They will allow to address biological and biomedical applications on sub-microsecond or even nanosecond time scales in force spectroscopy mode.
First optomechanical VLSI force probes on silicon have been designed and fabricated in LETI's industrial grade clean rooms and have led to first proofs of concept for fast AFM [1,2]. The post-doctoral student will be in charge of the preparation of force probes in order to integrate them in a high speed AFM developed by our partner at CNRS LAAS (Toulouse). He will be in charge of the back end operations, from the release of the structures, their observation (SEM, optical microscopies, etc.), to the optical packaging with fiber optic ferrules. He will also participate in the development of a test bench for components before and after packaging to select devices and validate the packaged probes before integration into an AFM.
The post-doctoral student will also investigate the operation of the probe in a liquid medium to allow later AFM studies of biological phenomena: for this, the development of efficient actuation means (electrostatic, thermal or optical) of the mechanical structure will be carried out and applied experimentally. A feedback on the modeling and the design is expected from the measurements, in order to ensure the understanding of the observed physical phenomena. Finally, the post-doctoral fellow will have the possibility to propose new device designs to target the expected performances. The devices will be fabricated in Leti's clean room, then tested and compared to the expected performances.
In the near future, emerging quantum information technologies are expected to lead to global breakthroughs in high performance computing and secure communication. Among semiconductor approaches, silicon-based spin quantum bits (qubits) are promising thanks to their compactness featuring long coherence time, high fidelity and fast qubit rotation [Maurand2016], [Meunier2019]. A main challenge is now to achieve individual qubit control inside qubit arrays.
Qubit array constitutes a compact open system, where each qubit cannot be considered as isolated since it depends on the neighboring qubit placement, their interconnection network and the back-end-line stack. The main goal of this post-doctoral position is to develop various implementation of spin control on 2D qubit array using multi-scale electromagnetic (EM) simulation ranging from nanometric single qubit up to millimetric interconnect network.
The candidate will i) characterize radio-frequency (RF) test structures at cryogenic temperature using state-of-the-art equipment and compare results with dedicated EM simulations, ii) evaluate the efficiency of spin control and allow multi-scale optimization from single to qubit arrays [Niquet2020], iii) integrate RF spin microwave control for 2D qubit array using CEA-LETI silicon technologies.
The candidate need to have a good RF and microelectronic background and experience in EM simulation, and/or design of RF test structures and RF characterization. This work takes place in a dynamic tripartite collaborative project between CEA-LETI, CEA-IRIG and CNRS-Institut Néel (ERC “Qucube”).