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1. (Digital Presentation) Oxide Memristors Based on SiO 2 with Cu/Ag Alloy Metallization for Neuromorphic Computing

   https://doi.org/10.1149/MA2022-0215808mtgabs  

   Qin, Fei ; Song, Han Wook ; Qin, Fei ( October 2022 , ECS Meeting Abstracts)

   By mimicking biomimetic synaptic processes, the success of artificial intelligence (AI) has been astounding with various applications such as driving automation, big data analysis, and natural-language processing.[1-4] Due to a large quantity of data transmission between the separated memory unit and the logic unit, the classical computing system with von Neumann architecture consumes excessive energy and has a significant processing delay.[5] Furthermore, the speed difference between the two units also causes extra delay, which is referred to as the memory wall.[6, 7] To keep pace with the rapid growth of AI applications, enhanced hardware systems that particularly feature an energy-efficient and high-speed hardware system need to be secured. The novel neuromorphic computing system, an in-memory architecture with low power consumption, has been suggested as an alternative to the conventional system. Memristors with analog-type resistive switching behavior are a promising candidate for implementing the neuromorphic computing system since the devices can modulate the conductance with cycles that act as synaptic weights to process input signals and store information.[8, 9]

   The memristor has sparked tremendous interest due to its simple two-terminal structure, including top electrode (TE), bottom electrode (BE), and an intermediate resistive switching (RS) layer. Many oxide materials, including HfO₂, Ta₂O₅, and IGZO, have extensively been studied as an RS layer of memristors. Silicon dioxide (SiO₂) features 3D structural conformity with the conventional CMOS technology and high wafer-scale homogeneity, which has benefited modern microelectronic devices as dielectric and/or passivation layers. Therefore, the use of SiO₂as a memristor RS layer for neuromorphic computing is expected to be compatible with current Si technology with minimal processing and material-related complexities.

   In this work, we proposed SiO₂-based memristor and investigated switching behaviors metallized with different reduction potentials by applying pure Cu and Ag, and their alloys with varied ratios. Heavily doped p-type silicon was chosen as BE in order to exclude any effects of the BE ions on the memristor performance. We previously reported that the selection of TE is crucial for achieving a high memory window and stable switching performance. According to the study which compares the roles of Cu (switching stabilizer) and Ag (large switching window performer) TEs for oxide memristors, we have selected the TE materials and their alloys to engineer the SiO₂-based memristor characteristics. The Ag TE leads to a larger memory window of the SiO₂memristor, but the device shows relatively large variation and less reliability. On the other hand, the Cu TE device presents uniform gradual switching behavior which is in line with our previous report that Cu can be served as a stabilizer, but with small on/off ratio.[9] These distinct performances with Cu and Ag metallization leads us to utilize a Cu/Ag alloy as the TE. Various compositions of Cu/Ag were examined for the optimization of the memristor TEs. With a Cu/Ag alloying TE with optimized ratio, our SiO₂based memristor demonstrates uniform switching behavior and memory window for analog switching applications. Also, it shows ideal potentiation and depression synaptic behavior under the positive/negative spikes (pulse train).

   In conclusion, the SiO₂memristors with different metallization were established. To tune the property of RS layer, the sputtering conditions of RS were varied. To investigate the influence of TE selections on switching performance of memristor, we integrated Cu, Ag and Cu/Ag alloy as TEs and compared the switch characteristics. Our encouraging results clearly demonstrate that SiO₂with Cu/Ag is a promising memristor device with synaptic switching behavior in neuromorphic computing applications.

   Acknowledgement

   This work was supported by the U.S. National Science Foundation (NSF) Award No. ECCS-1931088. S.L. and H.W.S. acknowledge the support from the Improvement of Measurement Standards and Technology for Mechanical Metrology (Grant No. 22011044) by KRISS.

   References

   [1] Younget al.,IEEE Computational Intelligence Magazine,vol. 13, no. 3, pp. 55-75, 2018.

   [2] Hadsellet al.,Journal of Field Robotics,vol. 26, no. 2, pp. 120-144, 2009.

   [3] Najafabadiet al.,Journal of Big Data,vol. 2, no. 1, p. 1, 2015.

   [4] Zhaoet al.,Applied Physics Reviews,vol. 7, no. 1, 2020.

   [5] Zidanet al.,Nature Electronics,vol. 1, no. 1, pp. 22-29, 2018.

   [6] Wulfet al.,SIGARCH Comput. Archit. News,vol. 23, no. 1, pp. 20–24, 1995.

   [7] Wilkes,SIGARCH Comput. Archit. News,vol. 23, no. 4, pp. 4–6, 1995.

   [8] Ielminiet al.,Nature Electronics,vol. 1, no. 6, pp. 333-343, 2018.

   [9] Changet al.,Nano Letters,vol. 10, no. 4, pp. 1297-1301, 2010.

   [10] Qinet al., Physica Status Solidi (RRL) - Rapid Research Letters, pssr.202200075R1, In press, 2022.

    

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2. Modular Simulation Framework for Process Variation Analysis of MRAM-based Deep Belief Networks

   Wood, Paul ; Pourmeidani, Hossein ; DeMara, Ronald F. ( March 2020 , IEEE SoutheastCon 2019)

   Magnetic Random-Access Memory (MRAM) based p-bit neuromorphic computing devices are garnering increasing interest as a means to compactly and efficiently realize machine learning operations in Restricted Boltzmann Machines (RBMs). When embedded within an RBM resistive crossbar array, the p-bit based neuron realizes a tunable sigmoidal activation function. Since the stochasticity of activation is dependent on the energy barrier of the MRAM device, it is essential to assess the impact of process variation on the voltage-dependent behavior of the sigmoid function. Other influential performance factors arise from varying energy barriers on power consumption requiring a simulation environment to facilitate the multi-objective optimization of device and network parameters. Herein, transportable Python scripts are developed to analyze the output variation under changes in device dimensions on the accuracy of machine learning applications. Evaluation with RBM circuits using the MNIST dataset reveal impacts and limits for processing variation of device fabrication in terms of the resulting energy vs. accuracy tradeoffs, and the resulting simulation framework is available via a Creative Commons license. 

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3. Adaptive cognition implemented with a context-aware and flexible neuron for next-generation artificial intelligence

   https://doi.org/10.1093/pnasnexus/pgac206  

   Jadaun, Priyamvada ; Cui, Can ; Liu, Sam ; Incorvia, Jean Anne C. ; Nelson, ed., Karen E. ( September 2022 , PNAS Nexus)

   Neuromorphic computing mimics the organizational principles of the brain in its quest to replicate the brain’s intellectual abilities. An impressive ability of the brain is its adaptive intelligence, which allows the brain to regulate its functions “on the fly” to cope with myriad and ever-changing situations. In particular, the brain displays three adaptive and advanced intelligence abilities of context-awareness, cross frequency coupling, and feature binding. To mimic these adaptive cognitive abilities, we design and simulate a novel, hardware-based adaptive oscillatory neuron using a lattice of magnetic skyrmions. Charge current fed to the neuron reconfigures the skyrmion lattice, thereby modulating the neuron’s state, its dynamics and its transfer function “on the fly.” This adaptive neuron is used to demonstrate the three cognitive abilities, of which context-awareness and cross-frequency coupling have not been previously realized in hardware neurons. Additionally, the neuron is used to construct an adaptive artificial neural network (ANN) and perform context-aware diagnosis of breast cancer. Simulations show that the adaptive ANN diagnoses cancer with higher accuracy while learning faster and using a more compact and energy-efficient network than a nonadaptive ANN. The work further describes how hardware-based adaptive neurons can mitigate several critical challenges facing contemporary ANNs. Modern ANNs require large amounts of training data, energy, and chip area, and are highly task-specific; conversely, hardware-based ANNs built with adaptive neurons show faster learning, compact architectures, energy-efficiency, fault-tolerance, and can lead to the realization of broader artificial intelligence.

    

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4. Stochastic Computing Architectures for Lightweight LSTM Neural Networks

   Sengupta, Roshwin ; Polian, Ilia ; Hayes, John P. ( April 2022 , 2022 25th International Symposium on Design and Diagnostics of Electronic Circuits & Systems (DDECS))

   For emerging edge and near-sensor systems to perform hard classification tasks locally, they must avoid costly communication with the cloud. This requires the use of compact classifiers such as recurrent neural networks of the long short term memory (LSTM) type, as well as a low-area hardware technology such as stochastic computing (SC). We study the benefits and costs of applying SC to LSTM design. We consider a design space spanned by fully binary (non-stochastic), fully stochastic, and several hybrid (mixed) LSTM architectures, and design and simulate examples of each. Using standard classification benchmarks, we show that area and power can be reduced up to 47% and 86% respectively with little or no impact on classification accuracy. We demonstrate that fully stochastic LSTMs can deliver acceptable accuracy despite accumulated errors. Our results also suggest that ReLU is preferable to tanh as an activation function in stochastic LSTMs 

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5. Harnessing the Metal–Insulator Transition of VO 2 in Neuromorphic Computing

   https://doi.org/10.1002/adma.202205294  

   Schofield, Parker ; Bradicich, Adelaide ; Gurrola, Rebeca M. ; Zhang, Yuwei ; Brown, Timothy D. ; Pharr, Matt ; Shamberger, Patrick J. ; Banerjee, Sarbajit ( November 2022 , Advanced Materials)

   Future‐generation neuromorphic computing seeks to overcome the limitations of von Neumann architectures by colocating logic and memory functions, thereby emulating the function of neurons and synapses in the human brain. Despite remarkable demonstrations of high‐fidelity neuronal emulation, the predictive design of neuromorphic circuits starting from knowledge of material transformations remains challenging. VO₂is an attractive candidate since it manifests a near‐room‐temperature, discontinuous, and hysteretic metal–insulator transition. The transition provides a nonlinear dynamical response to input signals, as needed to construct neuronal circuit elements. Strategies for tuning the transformation characteristics of VO₂based on modification of material properties, interfacial structure, and field couplings, are discussed. Dynamical modulation of transformation characteristics through in situ processing is discussed as a means of imbuing synaptic function. Mechanistic understanding of site‐selective modification; external, epitaxial, and chemical strain; defect dynamics; and interfacial field coupling in modifying local atomistic structure, the implications therein for electronic structure, and ultimately, the tuning of transformation characteristics, is emphasized. Opportunities are highlighted for inverse design and for using design principles related to thermodynamics and kinetics of electronic transitions learned from VO₂to inform the design of new Mott materials, as well as to go beyond energy‐efficient computation to manifest intelligence.

    

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