Tag Archives: Feng Lin

A nontraditional artificial synaptic device and roadmap for Chinese research into neuromorphic devices

A November 9, 2022 Science China Press press release on EurekAlert announces a new approach to developing neuromorphic (brainlike) devices,

Neuromorphic computing is an information processing model that simulates the efficiency of the human brain with multifunctionality and flexibility. Currently, artificial synaptic devices represented by memristors have been extensively used in neural morphological computing, and different types of neural networks have been developed. However, it is time-consuming and laborious to perform fixing and redeploying of weights stored by traditional artificial synaptic devices. Moreover, synaptic strength is primarily reconstructed via software programming and changing the pulse time, which can result in low efficiency and high energy consumption in neural morphology computing applications.

In a novel research article published in the Beijing-based National Science Review, Prof. Lili Wang from the Chinese Academy of Sciences and her colleagues present a novel hardware neural network based on a tunable flexible MXene energy storage (FMES) system. The system comprises flexible postsynaptic electrodes and MXene nanosheets, which are connected with the presynaptic electrodes using electrolytes. The potential changes in the ion migration process and adsorption in the supercapacitor can simulate information transmission in the synaptic gap. Additionally, the voltage of the FMES system represents the synaptic weight of the connection between two neurons.

Researchers explored the changes of paired-pulse facilitation under different resistance levels to investigate the effect of resistance on the advanced learning and memory behavior of the artificial synaptic system of FMES. The results revealed that the larger the standard deviation, the stronger the memory capacity of the system. In other words, with the continuous improvement of electrical resistance and stimulation time, the memory capacity of the artificial synaptic system of FMES is gradually improved. Therefore, the system can effectively control the accumulation and dissipation of ions by regulating the resistance value in the system without changing the external stimulus, which is expected to realize the coupling of sensing signals and storage weight.

The FMES system can be used to develop neural networks and realize various neural morphological computing tasks, making the recognition accuracy of handwritten digit sets reach 95%. Additionally, the FMES system can simulate the adaptivity of the human brain to achieve adaptive recognition of similar target data sets. Following the training process, the adaptive recognition accuracy can reach approximately 80%, and avoid the time and energy loss caused by recalculation.

“In the future, based on this research, different types of sensors can be integrated on the chip to further realize multimodal sensing computing integrated architecture.” Prof. Lili Wang stated, “The device can perform low-energy calculations, and is expected to solve the problems of high write noise, nonlinear difference, and diffusion under zero bias voltage in certain neural morphological systems.”

Here’s a link to and a citation for the paper,

Neuromorphic-computing-based adaptive learning using ion dynamics in flexible energy storage devices by Shufang Zhao, Wenhao Ran, Zheng Lou, Linlin Li, Swapnadeep Poddar, Lili Wang, Zhiyong Fan, Guozhen Shen. National Science Review, Volume 9, Issue 11, November 2022, nwac158, EOI: https://doi.org/10.1093/nsr/nwac158 Published: 13 August 2022

This paper is open access.

The future (or roadmap for) of Chinese research on neuromorphic engineering

While I was trying (unsuccessfully) to find a copy of the press release on the issuing agency’s website, I found this paper,

2022 roadmap on neuromorphic devices & applications research in China by Qing Wan, Changjin Wan, Huaqiang Wu, Yuchao Yang, Xiaohe Huang, Peng Zhou, LinChen, Tian-Yu Wang, Yi Li, Kanhao Xue, Yuhui He, Xiangshui Miao, Xi Li, Chenchen Xie, Houpeng Chen, Z. T. Song, Hong Wang, Yue Hao, Junyao Zhang, Jia Huang, Zheng Yu Ren, Li Qiang Zhu, Jianyu Du, Chen Ge, Yang Liu, Guanglong Ding, Ye Zhou, Su-Ting Han, Guosheng Wang, Xiao Yu, Bing Chen, Zhufei Chu, Lunyao Wang, Yinshui Xia, Chen Mu, Feng Lin, Chixiao Chen, Bojun Cheng, Yannan Xing, Weitao Zeng, Hong Chen, Lei Yu, Giacomo Indiveri and Ning Qiao. Neuromorphic Computing and Engineering DOI: 10.1088/2634-4386/ac7a5a *Accepted Manuscript online 20 June 2022 • © 2022 The Author(s). Published by IOP Publishing Ltd

The paper is open access.

*From the IOP’s Definitions of article versions: Accepted Manuscript is ‘the version of the article accepted for publication including all changes made as a result of the peer review process, and which may also include the addition to the article by IOP of a header, an article ID, a cover sheet and/or an ‘Accepted Manuscript’ watermark, but excluding any other editing, typesetting or other changes made by IOP and/or its licensors’.*

This is neither the published version nor the version of record.

Not ageing gracefully; the lithium-ion battery story

There’s an alphabet soup’s worth of agencies involved in research on lithium-ion battery ageing which has resulted in two papers as noted in a May 30, 2014 news item Azonano,

Batteries do not age gracefully. The lithium ions that power portable electronics cause lingering structural damage with each cycle of charge and discharge, making devices from smartphones to tablets tick toward zero faster and faster over time. To stop or slow this steady degradation, scientists must track and tweak the imperfect chemistry of lithium-ion batteries with nanoscale precision.

In two recent Nature Communications papers, scientists from several U.S. Department of Energy national laboratories—Lawrence Berkeley, Brookhaven, SLAC, and the National Renewable Energy Laboratory—collaborated to map these crucial billionths-of-a-meter dynamics and lay the foundation for better batteries.

A May 29, 2014 Brookhaven National Laboratory news release by Justin Eure, which originated the news item, describes the research techniques in more detail,

“We discovered surprising and never-before-seen evolution and degradation patterns in two key battery materials,” said Huolin Xin, a materials scientist at Brookhaven Lab’s Center for Functional Nanomaterials (CFN) and coauthor on both studies. “Contrary to large-scale observation, the lithium-ion reactions actually erode the materials non-uniformly, seizing upon intrinsic vulnerabilities in atomic structure in the same way that rust creeps unevenly across stainless steel.”

Xin used world-leading electron microscopy techniques in both studies to directly visualize the nanoscale chemical transformations of battery components during each step of the charge-discharge process. In an elegant and ingenious setup, the collaborations separately explored a nickel-oxide anode and a lithium-nickel-manganese-cobalt-oxide cathode—both notable for high capacity and cyclability—by placing samples inside common coin-cell batteries running under different voltages.

“Armed with a precise map of the materials’ erosion, we can plan new ways to break the patterns and improve performance,” Xin said.

In these experiments, lithium ions traveled through an electrolyte solution, moving into an anode when charging and a cathode when discharging. The processes were regulated by electrons in the electrical circuit, but the ions’ journeys—and the battery structures—subtly changed each time.

The news release first describes the research involving the nickel-oxide anode, one of the two areas of interest,

For the nickel-oxide anode, researchers submerged the batteries in a liquid organic electrolyte and closely controlled the charging rates. They stopped at predetermined intervals to extract and analyze the anode. Xin and his collaborators rotated 20-nanometer-thick sheets of the post-reaction material inside a carefully calibrated transmission electron microscope (TEM) grid at CFN to catch the contours from every angle—a process called electron tomography.

To see the way the lithium-ions reacted with the nickel oxide, the scientists used a suite of custom-written software to digitally reconstruct the three-dimensional nanostructures with single-nanometer resolution. Surprisingly, the reactions sprang up at isolated spatial points rather than sweeping evenly across the surface.

“Consider the way snowflakes only form around tiny particles or bits of dirt in the air,” Xin said. “Without an irregularity to glom onto, the crystals cannot take shape. Our nickel oxide anode only transforms into metallic nickel through nanoscale inhomogeneities or defects in the surface structure, a bit like chinks in the anode’s armor.”

The electron microscopy provided a crucial piece of the larger puzzle assembled in concert with Berkeley Lab materials scientists and soft x-ray spectroscopy experiments conducted at SLAC’s Stanford Synchrotron Radiation Lightsource (SSRL). The combined data covered the reactions on the nano-, meso-, and microscales.

Next, there’s this about the second area of interest, a lithium-nickel-manganese-cobalt-oxide (NMC) cathode (from the news release),

In the other study, scientists sought the voltage sweet-spot for the high-performing lithium-nickel-manganese-cobalt-oxide (NMC) cathode: How much power can be stored, at what intensity, and across how many cycles?

The answers hinged on intrinsic material qualities and the structural degradation caused by cycles at 4.7 volts and 4.3 volts, as measured against a lithium metal standard.

As revealed through another series of coin-cell battery tests, 4.7 volts caused rapid decomposition of the electrolytes and poor cycling—the higher power comes at a price. A 4.3-volt battery, however, offered a much longer cycling lifetime at the cost of lower storage and more frequent recharges.

In both cases, the chemical evolution exhibited sprawling surface asymmetries, though not without profound patterns.

“As the lithium ions race through the reaction layers, they cause clumping crystallization—a kind of rock-salt matrix builds up over time and begins limiting performance,” Xin said. “We found that these structures tended to form along the lithium-ion reaction channels, which we directly visualized under the TEM. The effect was even more pronounced at higher voltages, explaining the more rapid deterioration.”

Identifying this crystal-laden reaction pathways hints at a way forward in battery design.

“It may be possible to use atomic deposition to coat the NMC cathodes with elements that resist crystallization, creating nanoscale boundaries within the micron-sized powders needed at the cutting-edge of industry,” Xin said. “In fact, Berkeley Lab battery experts Marca Doeff and Feng Lin are working on that now.”

Shirley Meng, a professor at UC San Diego’s Department of NanoEngineering, added, “This beautiful study combines several complementary tools that probe both the bulk and surface of the NMC layered oxide—one of the most promising cathode materials for high-voltage operation that enables higher energy density in lithium-ion batteries. The meaningful insights provided by this study will significantly impact the optimization strategies for this type of cathode material.”

The TEM measurements revealed the atomic structures while electron energy loss spectroscopy helped pinpoint the chemical evolution—both carried out at the CFN….

The scientists next want to observe these changes in real-time which will necessitate the custom design of some new equipment (“electrochemical contacts and liquid flow holders”).

Here are links to and citations for the papers,

Phase evolution for conversion reaction electrodes in lithium-ion batteries by Feng Lin, Dennis Nordlund, Tsu-Chien Weng, Ye Zhu, Chunmei Ban, Ryan M. Richards, & Huolin L. Xin. Nature Communications 5, Article number: 3358 doi:10.1038/ncomms4358 Published 24 February 2014

Surface reconstruction and chemical evolution of stoichiometric layered cathode materials for lithium-ion batteries by Feng Lin, Isaac M. Markus, Dennis Nordlund, Tsu-Chien Weng, Mark D. Asta, Huolin L. Xin & Marca M. Doeff. Nature Communications 5, Article number: 3529 doi:10.1038/ncomms4529 Published 27 March 2014

Both of these articles are behind a paywall and they both offer previews via ReadCube Access.