Underline indicate Li+ group member. Italics indicate undergraduate student. *indicate equal contribution.
If you do not have access to the journal, you can likely Google the name to find an accepted version, or email Yiyang to request a preprint.
Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel. Nature, 621, 289–294 (2023)
Hongbo Zhao, Haitao Dean Deng, Alexander E. Cohen, Jongwoo Lim, Yiyang Li, Dimitrios Fraggedakis, Benben Jiang, Brian D. Storey, William C. Chueh, Richard D. Braatz, Martin Z. Bazant
Direct measurements of size-independent lithium diffusion and reaction times in individual polycrystalline battery particles. Energy and Environmental Sciences, 16, 3847-3859 (2023)
Jinhong Min, Lindsay M. Gubow, Riley J. Hargrave, Jason B. Siegel, Yiyang Li
Thermodynamic Origins of Nonvolatility in Resistive Switching (preprint)
Jingxian Li, Anirduh Appachar, Sabrina Peczonczyk, Elisa Harrison, Brianna Roest, Anton Ievlev, Ryan Hood, Sangmin Yoo, Kai Sun, Alec Talin, Wei Lu, Suhas Kumar, Wenhao Sun, Yiyang Li
Nonvolatile Electrochemical Random-Access Memory Under Short Circuit. Advanced Electronic Materials 9, 2200958 (2022)
Diana Kim, Virgil Watkins, Laszlo Cline, Jingxian Li, Kai Sun, Joshua D. Sugar, Elliot J. Fuller, A. Alec Talin, Yiyang Li
Highly Efficient Uniaxial In‐Plane Stretching of a 2D Material via Ion Insertion Advanced Materials, 33, 2101875 (2021)
Philipp K Muscher, Daniel A Rehn, Aditya Sood, Kipil Lim, Duan Luo, Xiaozhe Shen, Marc Zajac, Feiyu Lu, Apurva Mehta, Yiyang Li, Xijie Wang, Evan J Reed, William C Chueh, Aaron M Lindenberg
On-chip dynamic strain engineering requires efficient micro-actuators that can generate large in-plane strains. Inorganic electrochemical actuators are unique in that they are driven by low voltages (≈1 V) and produce considerable strains (≈1%). However, actuation speed and efficiency are limited by mass transport of ions. Minimizing the number of ions required to actuate is thus key to enabling useful “straintronic” devices. Here, it is shown that the electrochemical intercalation of exceptionally few lithium ions into WTe2 causes large anisotropic in-plane strain: 5% in one in-plane direction and 0.1% in the other. This efficient stretching of the 2D WTe2 layers contrasts to intercalation-induced strains in related materials which are predominantly in the out-of-plane direction. The unusual actuation of LixWTe2 is linked to the formation of a newly discovered crystallographic phase, referred to as Td', with an exotic atomic arrangement. On-chip low-voltage (<0.2 V) control is demonstrated over the transition to the novel phase and its composition. Within the Td'-Li0.5−δWTe2 phase, a uniaxial in-plane strain of 1.4% is achieved with a change of δ of only 0.075. This makes the in-plane chemical expansion coefficient of Td'-Li0.5−δWTe2 far greater than of any other single-phase material, enabling fast and efficient planar electrochemical actuation.
Low‐Voltage Electrochemical LixWO3 Synapses with Temporal Dynamics for Spiking Neural Networks, Advanced Intelligent Systems, 3, 2100021 (2021)
Qingzhou Wan, Marco Rasetto, Mohammad T Sharbati, John R Erickson, Sridhar Reddy Velagala, Matthew T Reilly, Yiyang Li, Ryad Benosman, Feng Xiong
Neuromorphic computing has the great potential to enable faster and more energy-efficient computing by overcoming the von Neumann bottleneck. However, most emerging nonvolatile memory (NVM)-based artificial synapses suffer from insufficient precision, nonlinear synaptic weight update, high write voltage, and high switching latency. Moreover, the spatiotemporal dynamics, an important temporal component for cognitive computing in spiking neural networks (SNNs), are hard to generate with existing complementary metal–oxide–semiconductor (CMOS) devices or emerging NVM. Herein, a three-terminal, LixWO3-based electrochemical synapse (LiWES) is developed with low programming voltage (0.2 V), fast programming speed (500 ns), and high precision (1024 states) that is ideal for artificial neural networks applications. Time-dependent synaptic functions such as paired-pulse facilitation (PPF) and temporal filtering that are critical for SNNs are also demonstrated. In addition, by leveraging the spike-encoded timing information extracted from the short-term plasticity (STP) behavior in the LiWES, an SNNs model is built to benchmark the pattern classification performance of the LiWES, and the result indicates a large boost in classification performance (up to 128×), compared with those NO-STP synapses.
In situ Parallel Training of Analog Neural Network Using Electrochemical Random-Access Memory, Frontiers of Neuroscience, 15, 636127 (2021)
Yiyang Li, T Patrick Xiao, Christopher H Bennett, Erik Isele, Armantas Melianas, Hanbo Tao, Matthew J Marinella, Alberto Salleo, Elliot J Fuller, A Alec Talin
In-memory computing based on non-volatile resistive memory can significantly improve the energy efficiency of artificial neural networks. However, accurate in situ training has been challenging due to the nonlinear and stochastic switching of the resistive memory elements. One promising analogue memory is the electrochemical random-access memory (ECRAM), also known as the redox transistor. Its low write currents and linear switching properties across hundreds of analogue states enable accurate and massively parallel updates of a full crossbar array, which yield rapid and energy-efficient training. While simulations predict that ECRAM based neural networks achieve high training accuracy at significantly higher speed and energy efficiency than digital implementations, these predictions have not been experimentally achieved. In this work, we train a 3×3 array of ECRAM devices that learns to discriminate several elementary logic gates (AND, OR, NAND). We record the evolution of the network’s synaptic weights during parallel in situ (on-line) training, with an outer product update. Due to linear and reproducible device switching characteristics, our crossbar simulations not only accurately simulate the epochs to convergence, but also quantitatively capture the evolution of weights in individual devices. The implementation of the first in situ parallel training together with strong agreement with simulation results provides a significant advance towards developing ECRAM into larger crossbar arrays for artificial neural network accelerators, which could enable orders of magnitude improvements in energy efficiency of deep neural networks.
Filament‐Free Bulk Resistive Memory Enables Deterministic Analogue Switching, Advanced Materials, 32, 2003984 (2020)
Yiyang Li, Elliot J Fuller, Joshua D Sugar, Sangmin Yoo, David S Ashby, Christopher H Bennett, Robert D Horton, Michael S Bartsch, Matthew J Marinella, Wei D Lu, A Alec Talin
Digital computing is nearing its physical limits as computing needs and energy consumption rapidly increase. Analogue-memory-based neuromorphic computing can be orders of magnitude more energy efficient at data-intensive tasks like deep neural networks, but has been limited by the inaccurate and unpredictable switching of analogue resistive memory. Filamentary resistive random access memory (RRAM) suffers from stochastic switching due to the random kinetic motion of discrete defects in the nanometer-sized filament. In this work, this stochasticity is overcome by incorporating a solid electrolyte interlayer, in this case, yttria-stabilized zirconia (YSZ), toward eliminating filaments. Filament-free, bulk-RRAM cells instead store analogue states using the bulk point defect concentration, yielding predictable switching because the statistical ensemble behavior of oxygen vacancy defects is deterministic even when individual defects are stochastic. Both experiments and modeling show bulk-RRAM devices using TiO2-X switching layers and YSZ electrolytes yield deterministic and linear analogue switching for efficient inference and training. Bulk-RRAM solves many outstanding issues with memristor unpredictability that have inhibited commercialization, and can, therefore, enable unprecedented new applications for energy-efficient neuromorphic computing. Beyond RRAM, this work shows how harnessing bulk point defects in ionic materials can be used to engineer deterministic nanoelectronic materials and devices.
Before 2020 (selected publications)
Low-voltage, CMOS-free synaptic memory based on LiXTiO2 redox transistors. ACS Applied Materials Interfaces, 11, 38982 (2019)
Y. Li, E. J. Fuller, S. Asapu, S. Agarwal, T. Kurita, J. J. Yang, A. A. Talin
Neuromorphic computers based on analogue neural networks aim to substantially lower computing power by reducing the need to shuttle data between memory and logic units. Artificial synapses containing nonvolatile analogue conductance states enable direct computation using memory elements; however, most nonvolatile analogue memories require high write voltages and large current densities and are accompanied by nonlinear and unpredictable weight updates. Here, we develop an inorganic redox transistor based on electrochemical lithium-ion insertion into LiXTiO2 that displays linear weight updates at both low current densities and low write voltages. The write voltage, as low as 200 mV at room temperature, is achieved by minimizing the open-circuit voltage and using a low-voltage diffusive memristor selector. We further show that the LiXTiO2 redox transistor can achieve an extremely sharp transistor subthreshold slope of just 40 mV/decade when operating in an electrochemically driven phase transformation regime.
Dynamic tuning of gap plasmon resonances using a solid-state electrochromic device. Nano Letters, 19, 7988 (2019)
Y. Li*, J. van de Groep*, A. A. Talin, M. L. Brongersma.
Plasmonic antennas and metasurfaces can effectively control light–matter interactions, and this facilitates a deterministic design of optical materials properties, including structural color. However, these optical properties are generally fixed after synthesis and fabrication, while many modern-day optics applications require active, low-power, and nonvolatile tuning. These needs have spurred broad research activities aimed at identifying materials and resonant structures capable of achieving large, dynamic changes in optical properties, especially in the challenging visible spectral range. In this work, we demonstrate dynamic tuning of polarization-dependent gap plasmon resonators that contain the electrochromic oxide WO3. Its refractive index in the visible changes continuously from n = 2.1 to 1.9 upon electrochemical lithium insertion and removal in a solid-state device. By incorporating WO3 into a gap plasmon resonator, the resonant wavelength can be shifted continuously and reversibly by up to 58 nm with less than 2 V electrochemical bias voltage. The resonator can remain in a tuned state for tens of minutes under open circuit conditions.
Parallel programming of an ionic floating-gate memory array for scaleable neuromorphic computing. Science, 364, 570 (2019)
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.
Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional computing through parallel programming and readout of artificial neural network weights in a crossbar memory array. However, selective and linear weight updates and <10-nanoampere read currents are required for learning that surpasses conventional computing efficiency. We introduce an ionic floating-gate memory array based on a polymer redox transistor connected to a conductive-bridge memory (CBM). Selective and linear programming of a redox transistor array is executed in parallel by overcoming the bridging threshold voltage of the CBMs. Synaptic weight readout with currents <10 nanoamperes is achieved by diluting the conductive polymer with an insulator to decrease the conductance. The redox transistors endure >1 billion write-read operations and support >1-megahertz write-read frequencies.
Fluid-enhanced surface diffusion controls intra-particle phase transformations. Nature Materials, 17, 915 (2018)
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. Basant, W. C. Chueh.
The kinetics and uniformity of ion insertion reactions at the solid-liquid interface govern the rate capability and lifetime, respectively, of electrochemical devices such as Li-ion batteries. Using an operando x-ray microscopy platform that maps the dynamics of the Li composition and insertion rate in LixFePO4, we found that nanoscale spatial variations in rate and in composition control the lithiation pathway at the subparticle length scale. Specifically, spatial variations in the insertion rate constant lead to the formation of nonuniform domains, and the composition dependence of the rate constant amplifies nonuniformities during delithiation but suppresses them during lithiation, and moreover stabilizes the solid solution during lithiation. This coupling of lithium composition and surface reaction rates controls the kinetics and uniformity during electrochemical ion insertion.