Phase-change memory devices for emerging computing paradigms
We are inviting applications from students to conduct their Master thesis work at IBM Research – Zurich on this exciting new topic. The goal of the project is to develop and optimize phase change memory (PCM) [1,2] for non-von Neumann computing. The work will involve experimental characterization and modeling (physical as well as behavioral) of PCM devices. It also involves interacting with researchers across IBM research focusing on various aspects of the project such as device fabrication, circuit design and algorithmic development
-  M. Le Gallo and A. Sebastian, “An overview of phase-change memory device physics”, J. Phys. D: Appl. Phys. (2020) https://iopscience.iop.org/article/10.1088/1361-6463/ab7794
-  M. Salinga, B. Kersting et al., “Monatomic phase change memory”, Nature Materials (2018) https://www.nature.com/articles/s41563-018-0110-9
The Big Picture
For decades, conventional computers based on the von Neumann architecture have performed computation by repeatedly transferring data between their processing and their memory units, which are physically separated. As computation becomes increasingly data-centric and as the scalability limits in terms of performance and power are being reached, alternative computing paradigms are searched for in which computation and storage are collocated. A fascinating new approach is that of in-memory computing where the physics of nanoscale memory devices are used to perform certain computational tasks within the memory unit akin to how biological synapses compute. In-memory computing is finding applications in a variety of application areas such as machine learning and signal processing . Most importantly, it is very appealing for making energy�efficient deep learning inference hardware, where the neural network layers would be encoded in crossbar arrays of memory devices . However, there are several challenges that need to be overcome at both hardware and algorithmic = levels to realize reliable and accurate inference engines based on computational memory.
-  A. Sebastian et al. Memory devices and applications for in-memory computing. Nature Nanotechnology (2020). https://doi.org/10.1038/s41565-020-0655-z
-  V. Joshi et al. Accurate deep learning inference using computational phase-change memory. Nature Communications (2020). https://www.nature.com/articles/s41467-020-16108-9
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. Benedikt Kersting, Dr. Abu Sebastian
- ETH Contact: Mathieu Luisier