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- ItemTowards Bacteria Counting in DI Water of Several Microliters or Growing Suspension Using Impedance Biochips(Basel : MDPI, 2020) Kiani, Mahdi; Tannert, Astrid; Du, Nan; Hübner, Uwe; Skorupa, Ilona; Bürger, Danilo; Zhao, Xianyue; Blaschke, Daniel; Rebohle, Lars; Cherkouk, Charaf; Neugebauer, Ute; Schmidt, Oliver G.; Schmidt, HeidemarieWe counted bacterial cells of E. coli strain K12 in several-microliter DI water or in several-microliter PBS in the low optical density (OD) range (OD = 0.05–1.08) in contact with the surface of Si-based impedance biochips with ring electrodes by impedance measurements. The multiparameter fit of the impedance data allowed calibration of the impedance data with the concentration cb of the E. coli cells in the range of cb = 0.06 to 1.26 × 109 cells/mL. The results showed that for E. coli in DI water and in PBS, the modelled impedance parameters depend linearly on the concentration of cells in the range of cb = 0.06 to 1.26 × 109 cells/mL, whereas the OD, which was independently measured with a spectrophotometer, was only linearly dependent on the concentration of the E. coli cells in the range of cb = 0.06 to 0.50 × 109 cells/mL.
- ItemLow-power emerging memristive designs towards secure hardware systems for applications in internet of things(Amsterdam : Elsevier, 2021) Du, Nan; Schmidt, Heidemarie; Polian, IliaEmerging memristive devices offer enormous advantages for applications such as non-volatile memories and in-memory computing (IMC), but there is a rising interest in using memristive technologies for security applications in the era of internet of things (IoT). In this review article, for achieving secure hardware systems in IoT, low-power design techniques based on emerging memristive technology for hardware security primitives/systems are presented. By reviewing the state-of-the-art in three highlighted memristive application areas, i.e. memristive non-volatile memory, memristive reconfigurable logic computing and memristive artificial intelligent computing, their application-level impacts on the novel implementations of secret key generation, crypto functions and machine learning attacks are explored, respectively. For the low-power security applications in IoT, it is essential to understand how to best realize cryptographic circuitry using memristive circuitries, and to assess the implications of memristive crypto implementations on security and to develop novel computing paradigms that will enhance their security. This review article aims to help researchers to explore security solutions, to analyze new possible threats and to develop corresponding protections for the secure hardware systems based on low-cost memristive circuit designs.
- ItemCharged domains in ferroelectric, polycrystalline yttrium manganite thin films resolved with scanning electron microscopy(Bristol : IOP Publ., 2020) Rayapati, Venkata Rao; Bürger, Danilo; Du, Nan; Kowol, Cornelia; Blaschke, Daniel; Stöcker, Hartmut; Matthes, Patrick; Patra, Rajkumar; Skorupa, Ilona; Schulz, Stefan E.; Schmidt, HeidemarieWe have investigated ferroelectric charged domains in polycrystalline hexagonal yttrium manganite thin films (Y1Mn1O3, Y0.95Mn1.05O3, Y1Mn0.99Ti0.01O3, and Y0.94Mn1.05Ti0.01O3) by scanning electron microscopy (SEM) in secondary electron emission mode with a small acceleration voltage. Using SEM at an acceleration voltage of 1.0 kV otherwise homogenous surface charging effects are reduced, polarization charges can be observed and polarization directions (±Pz) of the ferroelectric domains in the polycrystalline thin films can be identified. Thin films of different chemical composition have been deposited by pulsed laser deposition on Pt/SiO2/Si structures under otherwise same growth conditions. Using SEM it has been shown that different charged domain density networks are existing in polycrystalline yttrium manganite thin films. © 2020 IOP Publishing Ltd.
- ItemRedox Memristors with Volatile Threshold Switching Behavior for Neuromorphic Computing(Windsor ; Beijing : English China Online Journals, ECOJ, 2022) Wang, Yu-Hao; Gong, Tian-Cheng; Ding, Ya-Xin; Li, Yang; Wang, Wei; Chen, Zi-Ang; Du, Nan; Covi, Erika; Farronato, Matteo; Ielmini, Daniele; Zhang, Xu-Meng; Luo, QingThe spiking neural network (SNN), closely inspired by the human brain, is one of the most powerful platforms to enable highly efficient, low cost, and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system. In the hardware implementation, the building of artificial spiking neurons is fundamental for constructing the whole system. However, with the slowing down of Moore’s Law, the traditional complementary metal-oxide-semiconductor (CMOS) technology is gradually fading and is unable to meet the growing needs of neuromorphic computing. Besides, the existing artificial neuron circuits are complex owing to the limited bio-plausibility of CMOS devices. Memristors with volatile threshold switching (TS) behaviors and rich dynamics are promising candidates to emulate the biological spiking neurons beyond the CMOS technology and build high-efficient neuromorphic systems. Herein, the state-of-the-art about the fundamental knowledge of SNNs is reviewed. Moreover, we review the implementation of TS memristor-based neurons and their systems, and point out the challenges that should be further considered from devices to circuits in the system demonstrations. We hope that this review could provide clues and be helpful for the future development of neuromorphic computing with memristors.
- ItemSingle pairing spike-timing dependent plasticity in BiFeO3 memristors with a time window of 25 ms to 125 μs(Lausanne : Frontiers Research Foundation, 2015) Du, Nan; Kiani, Mahdi; Mayr, Christian G.; You, Tiangui; Bürger, Danilo; Skorupa, Ilona; Schmidt, Oliver G.; Schmidt, HeidemarieMemristive devices are popular among neuromorphic engineers for their ability to emulate forms of spike-driven synaptic plasticity by applying specific voltage and current waveforms at their two terminals. In this paper, we investigate spike-timing dependent plasticity (STDP) with a single pairing of one presynaptic voltage spike and one post-synaptic voltage spike in a BiFeO3 memristive device. In most memristive materials the learning window is primarily a function of the material characteristics and not of the applied waveform. In contrast, we show that the analog resistive switching of the developed artificial synapses allows to adjust the learning time constant of the STDP function from 25 ms to 125 μs via the duration of applied voltage spikes. Also, as the induced weight change may degrade, we investigate the remanence of the resistance change for several hours after analog resistive switching, thus emulating the processes expected in biological synapses. As the power consumption is a major constraint in neuromorphic circuits, we show methods to reduce the consumed energy per setting pulse to only 4.5 pJ in the developed artificial synapses.
- ItemSynaptic Plasticity in Memristive Artificial Synapses and Their Robustness Against Noisy Inputs(Lausanne : Frontiers Research Foundation, 2021) Du, Nan; Zhao, Xianyue; Chen, Ziang; Choubey, Bhaskar; Di Ventra, Massimiliano; Skorupa, Ilona; Bürger, Danilo; Schmidt, HeidemarieEmerging brain-inspired neuromorphic computing paradigms require devices that can emulate the complete functionality of biological synapses upon different neuronal activities in order to process big data flows in an efficient and cognitive manner while being robust against any noisy input. The memristive device has been proposed as a promising candidate for emulating artificial synapses due to their complex multilevel and dynamical plastic behaviors. In this work, we exploit ultrastable analog BiFeO3 (BFO)-based memristive devices for experimentally demonstrating that BFO artificial synapses support various long-term plastic functions, i.e., spike timing-dependent plasticity (STDP), cycle number-dependent plasticity (CNDP), and spiking rate-dependent plasticity (SRDP). The study on the impact of electrical stimuli in terms of pulse width and amplitude on STDP behaviors shows that their learning windows possess a wide range of timescale configurability, which can be a function of applied waveform. Moreover, beyond SRDP, the systematical and comparative study on generalized frequency-dependent plasticity (FDP) is carried out, which reveals for the first time that the ratio modulation between pulse width and pulse interval time within one spike cycle can result in both synaptic potentiation and depression effect within the same firing frequency. The impact of intrinsic neuronal noise on the STDP function of a single BFO artificial synapse can be neglected because thermal noise is two orders of magnitude smaller than the writing voltage and because the cycle-to-cycle variation of the current–voltage characteristics of a single BFO artificial synapses is small. However, extrinsic voltage fluctuations, e.g., in neural networks, cause a noisy input into the artificial synapses of the neural network. Here, the impact of extrinsic neuronal noise on the STDP function of a single BFO artificial synapse is analyzed in order to understand the robustness of plastic behavior in memristive artificial synapses against extrinsic noisy input.
- ItemP-N junction-based Si biochips with ring electrodes for novel biosensing applications(Basel : MDPI, 2019) Kiani, Mahdi; Du, Nan; Vogel, Manja; Raff, Johannes; Hübner, Uwe; Skorupa, Ilona; Bürger, Danilo; Schulz, Stefan E.; Schmidt, Oliver G.; Schmidt, HeidemarieIn this work, we report on the impedance of p-n junction-based Si biochips with gold ring top electrodes and unstructured platinum bottom electrodes which allows for counting target biomaterial in a liquid-filled ring top electrode region. The systematic experiments on p-n junction-based Si biochips fabricated by two different sets of implantation parameters (i.e. biochips PS5 and BS5) are studied, and the comparable significant change of impedance characteristics in the biochips in dependence on the number of bacteria suspension, i.e., Lysinibacillus sphaericus JG-A12, in Deionized water with an optical density at 600 nm from OD600 = 4–16 in the electrode ring region is demonstrated. Furthermore, with the help of the newly developed two-phase electrode structure, the modeled capacitance and resistance parameters of the electrical equivalent circuit describing the p-n junction-based biochips depend linearly on the number of bacteria in the ring top electrode region, which successfully proves the potential performance of p-n junction-based Si biochips in observing the bacterial suspension. The proposed p-n junction-based biochips reveal perspective applications in medicine and biology for diagnosis, monitoring, management, and treatment of diseases.In this work, we report on the impedance of p-n junction-based Si biochips with gold ring top electrodes and unstructured platinum bottom electrodes which allows for counting target biomaterial in a liquid-filled ring top electrode region. The systematic experiments on p-n junction-based Si biochips fabricated by two different sets of implantation parameters (i.e. biochips PS5 and BS5) are studied, and the comparable significant change of impedance characteristics in the biochips in dependence on the number of bacteria suspension, i.e., Lysinibacillus sphaericus JG-A12, in Deionized water with an optical density at 600 nm from OD600 = 4–16 in the electrode ring region is demonstrated. Furthermore, with the help of the newly developed two-phase electrode structure, the modeled capacitance and resistance parameters of the electrical equivalent circuit describing the p-n junction-based biochips depend linearly on the number of bacteria in the ring top electrode region, which successfully proves the potential performance of p-n junction-based Si biochips in observing the bacterial suspension. The proposed p-n junction-based biochips reveal perspective applications in medicine and biology for diagnosis, monitoring, management, and treatment of diseases.
- ItemEngineering interface-type resistive switching in BiFeO3 thin film switches by Ti implantation of bottom electrodes(London : Nature Publishing Group, 2015) You, Tiangui; Ou, Xin; Niu, Gang; Bärwolf, Florian; Li, Guodong; Du, Nan; Bürger, Danilo; Skorupa, Ilona; Jia, Qi; Yu, Wenjie; Wang, Xi; Schmidt, Oliver G.; Schmidt, HeidemarieBiFeO3 based MIM structures with Ti-implanted Pt bottom electrodes and Au top electrodes have been fabricated on Sapphire substrates. The resulting metal-insulator-metal (MIM) structures show bipolar resistive switching without an electroforming process. It is evidenced that during the BiFeO3 thin film growth Ti diffuses into the BiFeO3 layer. The diffused Ti effectively traps and releases oxygen vacancies and consequently stabilizes the resistive switching in BiFeO3 MIM structures. Therefore, using Ti implantation of the bottom electrode, the retention performance can be greatly improved with increasing Ti fluence. For the used raster-scanned Ti implantation the lateral Ti distribution is not homogeneous enough and endurance slightly degrades with Ti fluence. The local resistive switching investigated by current sensing atomic force microscopy suggests the capability of down-scaling the resistive switching cell to one BiFeO3 grain size by local Ti implantation of the bottom electrode.
- ItemPhysics inspired compact modelling of BiFeO3 based memristors([London] : Macmillan Publishers Limited, part of Springer Nature, 2022) Yarragolla, Sahitya; Du, Nan; Hemke, Torben; Zhao, Xianyue; Chen, Ziang; Polian, Ilia; Mussenbrock, ThomasWith the advent of the Internet of Things, nanoelectronic devices or memristors have been the subject of significant interest for use as new hardware security primitives. Among the several available memristors, BiFeO3 (BFO)-based electroforming-free memristors have attracted considerable attention due to their excellent properties, such as long retention time, self-rectification, intrinsic stochasticity, and fast switching. They have been actively investigated for use in physical unclonable function (PUF) key storage modules, artificial synapses in neural networks, nonvolatile resistive switches, and reconfigurable logic applications. In this work, we present a physics-inspired 1D compact model of a BFO memristor to understand its implementation for such applications (mainly PUFs) and perform circuit simulations. The resistive switching based on electric field-driven vacancy migration and intrinsic stochastic behaviour of the BFO memristor are modelled using the cloud-in-a-cell scheme. The experimental current–voltage characteristics of the BFO memristor are successfully reproduced. The response of the BFO memristor to changes in electrical properties, environmental properties (such as temperature) and stress are analyzed and consistant with experimental results.