Electrical characterization and optimization of electrochemical random-access memory for analog computing
IBM Research – Zurich has an opening for a Master Thesis in the area of electrical characterization and optimization of electrochemical devices for analog computing. The work will be carried out in the Science & Technology Department at IBM Research-Zurich and will involve material and device optimization.
Electrochemical random access memory (ECRAM) isa novel type of non-volatile memory (NVM) [1,2] to provide acceleration for training of deep neural networks (DNNs) . ECRAM is based on the reversible electrochemical intercalation of an ion into a host material, which causes a change in electrical resistance. Compared to other NVMs such as phase-change memory (PCM) or resistive random access memory (ReRAM), ECRAM provides more symmetric and deterministic potentiation and depression. However, a thorough understanding of ECRAM operation and the influence of materials properties and geometry on performance metrics requires a detailed multiphysics model
 Fuller, E. J. et al.Li-Ion Synaptic Transistor for Low Power Analog Computing. Adv. Mater.29, 1–8 (2017).
 Tang, J. et al.ECRAM as Scalable Synaptic Cell for High-Speed, Low-Power Neuromorphic Computing. in IEEE International Electron Devices Meeting (IEDM)(2018).
 Gokmen, T. & Vlasov, Y. Acceleration of deep neural network training with resistive cross-point devices: Design considerations. Front. Neurosci.10, 1–13 (2016).
Objectives & Methodology
An existing device concept and experimental work is available as starting point. The materials used for the devices have been developed and characterized. The objective of this project is to vary some material parameters (thickness and stoichiometry) and device layer stacks in order to optimize the ECRAM performances in terms of symmetry, resistance range and retention of potentiation/depression operations. The Master Thesis project consists in using pulsed electrical characterization and impedance spectroscopy measurements on the devices as feedback for the device optimization and for the understanding of the physics behind these ion intercalation-based devices. Familiarity with experimental characterization of electronic devices is desirable.
- Looking for 1 Master student
- Interested candidates please contact: Dr. Valeria Bragaglia
- ETH Contact: Mathieu Luisier