Research motivation and thrust

Human development and improved standards of living have historically coincided with increases in energy consumption. As billions of humans enter the global middle class, the environmental consequences of fossil energy use place immense strains of the earth’s ecosystem. It is necessary to decouple development with carbon emissions through energy efficiency and renewable energy. Electrochemical systems play crucial roles towards achieving these goals.

The Li+ group aims to advance fundamental understanding and develop technologies in electrochemical materials and devices. We study the underlying physical principles behind such materials, devices, and systems, and exploit them into developing new technologies towards energy solutions. We specifically investigate transition metal oxides with point defects. Point defects, such as lithium interstitials and oxygen vacancies, act as electronic dopants and enable these materials to conduct both electrons and ions, or “mixed conduction.” Mixed conduction combined with electrochemistry enables the simultaneous addition or removal of electron and ions using current and voltage, allowing us to add or remove ~1022 cm-3 dopants dynamically under device operating conditions. This process is known as electrochemical ion insertion, and is crucial for a number of technologies including batteries, electrochromics, solid fuel cells, electro-catalysts, etc. The two main thrusts of this group is to develop materials and devices for batteries and for in-memory, low-energy computing. 

We also aim to cross-pollinate and synergize the fields of electrochemistry and microelectronics. These fields are united by the crucial role of current and voltage in obtaining functionality. Key thrusts involve understanding electrochemical processes in the length, time, and operational environmental of microelectronics. We also seek to use the tools of microelectronics to better understand fundamental electrochemical processes.

Reference: Y. Li and W. C. Chueh, “Electrochemical and Chemical Insertion for Energy Transformation and Switching.” Annual Reviews for Materials Research. 48:137-165 (2018)

Electrochemical redox memory

Machine learning and artificial intelligence require large data centers, consume extraordinary amounts of energy, will soon be major contributors to climate change. How can we make AI more energy efficient?

The energy intensity of of data-heavy processes like AI result from the need to move information from logic to processor elements. Neural networks can be 1000x more energy efficient by computing on a memory crossbar using physics (Ohm’s & Kirchoff’s Laws) to perform matrix multiplications, the most ubiquitous process.

Neuromorphic comptuting containing crossbar arrays of non-volatile memory elements enable orders of magnitude reduction in the energy consumption of data-intensive processes like machine learning.

For the past several decades, most research in the nonvolatile memory element have focused onto filament-based information storage elements like memristors and phase-change memory. In such devices, information is stored in discrete numbers of point defects within the filament. Due to kinetic theory, individual atoms follow random, stochastic motion, leading to memory devices with irreproducible and stochastic switching.

Our approach is to use the average behavior of all point defects in the bulk. With over 106 lattice sites in a small (30-nm)3 volume, the average behavior of all point defects is statistical. Harnessing this statistical behavior results in predictable and deterministic analogue switching.


Filamentary and bulk resistive memory. (a) Conventional filamentary memory devices store analogue information states in atomic-sized filaments. (b) Such devices switch stochastically due to the discrete nature of atoms in the filament and kinetic theory. (c) Bulk memory instead stores information using the point defect concentration, a continuous variable. (d) Bulk memory yields deterministic switching.

We seek to both understand the fundamental ion insertion and migration processes within these redox memory cells, as well as design improved devices. We envision an analogue “neural-core” hardware accelerator than can be placed in all chips to accelerate machine learning in an energy-efficient manner.


Past work:

Y. Li, E. J. Fuller, J. D. Sugar, S. Yoo, D. S. Ashby, C. H. Bennett, R. D. Horton, M. S. Bartsch, M. J. Marinella, W. D. Lu, A. A. Talin. “Filament-free bulk resistive memory enables deterministic analogue switching.” Advanced Materials, Accepted (2020)

E. J. Fuller, S. T. Keene, A. Melianas, Z. Wang, S. Agarwal, Y. Li, Y. Tuchman, C. D. James, M. J. Marinella, J. J. Yang, A.Salleo, A. A. Talin. ”Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing.” Science. 364, 570-574 (2019)

Electrochemical and microstructural heterogeneity in Li-ion battery particles

A foundational principle in materials science is that the property of a material depends on its structure. While this relationship is almost certainly applicable to Li-ion batteries, much is unknown about the relation between the microstructure of individual battery particles (5-50 micron) that make up all battery electrodes, and the electrochemical performance of those particles. A key shortcoming is that microstructure is characterized on the single particle length scale, while electrochemistry is measured using a composite electrode containing an ensemble of at least 106 battery particles. Thus, it is extremely difficult to correlate the microstructure of a single particle or a few particles, with the electrochemistry of a composite electrode electrode.

We aim to bridge this missing length scale by conducting electrochemistry in individual battery particles. We have successfully assembled single-particle microelectrodes that enable the charge and discharge of individual particles with high throughput. Our work enables us to measure the electrochemical variability between battery particles, and correlate these to complementary microstructural measurements.

Charge and discharge profile for a single NMC and LCO particle.

Past work:

Y. Li, H. Chen, K. Lim, H. D. Deng, J. Lim, D. Fraggedakis, P. M. Attia, S. C. Lee, N. Jin, J. Moskon, Z. Guan, W. E. Gent, J. Hong, Y. S. Yu, M. Gaberscek, M. S. Islam, M. Z. Bazant, W. C. Chueh. ”Fluid-enhanced Surface Diffusion Controls Intra-Particle Phase Transformations.” Nature Materials. 17, 915-922 (2018)

J. Lim,* Y. Li,* D. H. Alsem, H. So, S. C. Lee, P. Bai, D. A. Cogswell, X. Liu, N. Jin, Y. Yu, N. J. Salmon, D. A. Shapiro, M. Z. Bazant, T. Tyliszczak, W. C. Chueh. “Origin and hysteresis of lithium compositional spatiodynamics in battery primary particles.” Science, 353, 566-71 (2016)