Nan Li

Bio: Nan Li is an academic researcher from National University of Defense Technology. The author has contributed to research in topics: Spiking neural network & Compressed sensing. The author has an hindex of 8, co-authored 58 publications receiving 200 citations. Previous affiliations of Nan Li include University of Sussex & École Polytechnique de Montréal.

A Memristor-Based Spiking Neural Network With High Scalability and Learning Efficiency

Abstract: Spike-timing dependent plasticity (STDP)-based spiking neural network (SNN) is a promising choice to realize unsupervised intelligent systems with a limited power budget. In addition to STDP, another two bio-inspired mechanisms of lateral inhibition and homeostasis are always implemented in the unsupervised training procedure of STDP-based SNNs. However, the existing methods to achieve lateral inhibition necessitate a great number of connections that are proportional to the square of the number of learning neurons, and the existing hardware solution of homeostasis demands complex circuits for each learning neuron, both of which challenge the hardware implementation of STDP-based SNNs. In this brief, we propose a novel SNN using memristor-based inhibitory synapses to realize the mechanisms of lateral inhibition and homeostasis with low hardware complexity. The proposed SNN can improve the network scalability by reducing the connection number for lateral inhibition from $N^{2}$ to $N$ and reduce the hardware overhead by leveraging the circuit of lateral inhibition to achieve homeostasis. Software simulations on the recognition task on MNIST dataset show that the proposed SNN achieves a ~ 2 times higher learning efficiency with comparable accuracy. In addition, the challenging properties of realistic memristor devices, including limited number of resistive states, intrinsic parameter variation, and permanent open device, are added in the simulation to evaluate the robustness of our proposed approach.

A high-speed bioelectrical impedance spectroscopy system based on the digital auto-balancing bridge method

Abstract: A novel bioelectrical impedance spectroscopy system based on the digital auto-balancing bridge method improved from the conventional analogue auto-balancing method is presented for bioelectrical impedance measurements. The hardware of the proposed system consists of a reference source, a null detector, a variable source, a field programmable gate array, a clock generator, a flash and a USB controller. Software implemented in the field programmable gate array includes three major blocks: clock management, peripheral control and digital signal processing. The principle and realization of the least-mean-squares-based digital auto-balancing algorithm is introduced in detail. The performances of our system were examined by comparing with a commercial impedance analyzer. The results reveal that the proposed system has high speed (less than 3.5 ms per measurement) and high accuracy in the frequency range of 1 kHz-10 MHz. Compared with the commercial instrument based on the traditional analogue auto-balancing method, our system shows advantages in measurement speed, compactness and flexibility, making it suitable for various bioelectrical impedance measurement applications.

Analysis of the Negative-SET Behaviors in Cu/ZrO2/Pt Devices.

Abstract: Metal oxide-based electrochemical metallization memory (ECM) shows promising performance for next generation non-volatile memory. The negative-SET behavior has been observed in various oxide-based ECM devices. But the underlying mechanism of this behavior remains unaddressed and the role of the metal cation and oxygen vacancy in this behavior is unclear. In this work, we have observed two kinds of negative-SET (labeled as N-SET1 and N-SET2) behaviors in our Cu/ZrO2/Pt devices. Both the two behaviors can result in hard breakdown due to the high compliance current in reset process. The I-V characteristic shows that the two negative-SET behaviors have an obvious difference in operation voltage. Using four-probe resistance measurement method, the resistance-temperature characteristics of the ON-state after various negative-SET behaviors have been studied. The temperature dependence results demonstrate that the N-SET1 behavior is dominated by Cu conductive filament (CF) reformation caused by the Cu CF overgrowth phenomenon while the N-SET2 is related to the formation of oxygen vacancy CF. This work may provide a comprehensive understanding of the switching mechanism in oxide-based ECM devices.

Highly improved resistive switching performances of the self-doped Pt/HfO2:Cu/Cu devices by atomic layer deposition

Abstract: Metal-oxide electrochemical metallization (ECM) memory is a promising candidate for the next generation nonvolatile memory But this memory suffers from large dispersion of resistive switching parameters due to the intrinsic randomness of the conductive filament In this work, we have proposed a self-doping approach to improve the resistive switching characteristics The fabricated Pt/HfO2:Cu/Cu device shows outstanding nonvolatile memory properties, including high uniformity, good endurance, long retention and fast switching speed The results demonstrate that the self-doping approach is an effective method to improve the metal-oxide ECM memory performances and the self-doped Pt/HfO2:Cu/Cu device has high potentiality for the nonvolatile memory applications in the future

Neuroscience 細胞死：最近の知見

Abstract: 中枢神経系疾患の治療は正常細胞（ニューロン）の機能維持を目的とするが，脳血管障害のように機能障害の原因が細胞の死滅に基づくことは多い．一方，脳腫瘍の治療においては薬物療法や放射線療法といった腫瘍細胞の死滅を目標とするものが大きな位置を占める．いずれの場合にも，細胞死の機序を理解することは各種病態や治療法の理解のうえで重要である．現在のところ最も研究の進んでいる細胞死の型はアポトーシスである．そのなかで重要な位置を占めるミトコンドリアにおける反応および抗アポトーシス因子について概要を紹介する．

Fundamentals, Recent Advances, and Future Challenges in Bioimpedance Devices for Healthcare Applications

Abstract: This work develops a thorough review of bioimpedance systems for healthcare applications. The basis and fundamentals of bioimpedance measurements are described covering issues ranging from the hardware diagrams to the configurations and designs of the electrodes and from the mathematical models that describe the frequency behavior of the bioimpedance to the sources of noise and artifacts. Bioimpedance applications such as body composition assessment, impedance cardiography (ICG), transthoracic impedance pneumography, electrical impedance tomography (EIT), and skin conductance are described and analyzed. A breakdown of recent advances and future challenges of bioimpedance is also performed, addressing topics such as transducers for biosensors and Lab-on-Chip technology, measurements in implantable systems, characterization of new parameters and substances, and novel bioimpedance applications.

A Batteryless Sensor ASIC for Implantable Bio-Impedance Applications

Abstract: The measurement of the biological tissue’s electrical impedance is an active research field that has attracted a lot of attention during the last decades. Bio-impedances are closely related to a large variety of physiological conditions; therefore, they are useful for diagnosis and monitoring in many medical applications. Measuring living tissues, however, is a challenging task that poses countless technical and practical problems, in particular if the tissues need to be measured under the skin. This paper presents a bio-impedance sensor ASIC targeting a battery-free, miniature size, implantable device, which performs accurate 4-point complex impedance extraction in the frequency range from 2 kHz to 2 MHz. The ASIC is fabricated in 150 nm CMOS, has a size of 1.22 mm $\, \times \,$ 1.22 mm and consumes 165 $\mu {\rm A}$ from a 1.8 V power supply. The ASIC is embedded in a prototype which communicates with, and is powered by an external reader device through inductive coupling. The prototype is validated by measuring the impedances of different combinations of discrete components, measuring the electrochemical impedance of physiological solution, and performing ex vivo measurements on animal organs. The proposed ASIC is able to extract complex impedances with around 1 $\Omega $ resolution; therefore enabling accurate wireless tissue measurements.

A New Approach for Readout of Resistive Sensor Arrays for Wearable Electronic Applications

Abstract: Resistive sensor arrays have been increasingly adopted in wearable electronic applications, which require low-complexity and low-energy circuits. However, current readout strategies for resistive sensor arrays require additional electrical components, such as transistors, diodes, multiplexers, op-amps, switches, current sources, and A/D converters, leading to a considerable increase in circuit complexity, power consumption, system instability, and crosstalk error. To address the problem, this paper proposes a new approach, which determines sensor resistance values by establishing and solving resistance matrix equations of sensor arrays. Unlike conventional approaches, it allows crosstalk currents in arrays to avoid additional components that are originally used for eliminating crosstalk currents and minimizing crosstalk error. Meanwhile, it takes advantage of on-chip resources of wearable platforms, thereby reducing redundant chips. It was implemented on a prototype of 10 × 10 textile resistive sensor array, which was taken in a sensing cushion for sitting pressure monitoring of chair bound people. Experimental results on this array platform showed the new approach achieved a satisfactory accuracy (0.61% ± 0.41%), as well as a low crosstalk error (2.77% ± 0.61%). The fabricated sensing cushion also exhibited a relatively low pressure measurement error (6.30% ± 0.75%). Compared with other approaches, the proposed approach demonstrated the lowest circuit complexity on a microcontroller based wearable platform, and a sufficient sensor capacity. It is ideal for a wide range of applications like wearable or implantable sensing, presenting a reference for the design of low-complexity and low-crosstalk error wearable systems based on resistive sensor arrays.