Custom measurement system for memristor characterisation

Lupo, F.A.; Scirè, Daniele; Mosca, Mauro; Crupi, I.; Razzari, Luca; Macaluso, Roberto

doi:10.1016/j.sse.2021.108049

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…Various signalgeneration and acquisition systems have been documented, with substantial differences in chip energy and footprint. Transimpedance amplifiers and analog-to-digital converters can sense analog currents, [501][502][503] whereas digital-to-analog converters can generate analogue voltage inputs. The energy efficiency of %50 TOPS W À1 with MM-based arrays has been demonstrated using on-chip current-sense amplifiers and digital inputs.…”

Section: Outlooks Challenges and Opportunitiesmentioning

confidence: 99%

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Go,

Lim,

Lee

et al. 2024

Small Science

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Separate memory and processing units are utilized in conventional von Neumann computational architectures. However, regarding the energy and the time, it is costly to shuffle data between the memory and the processing entity, and for data‐intensive applications associated with artificial intelligence, the demand is ever increasing. A paradigm shift in traditional architectures is required, and in‐memory computing is one of the non‐von‐Neumann computing strategies. By harnessing physical signatures of the memory, computing workloads are administered in the same memory element. For in‐memory computing, a wide range of memristive material (MM) systems have been examined. Moreover, developing computing schemes that perform in the same sensory network and that minimize the data shuffle between the processing unit and the sensing element is a requirement, to process large volumes of data efficiently and decrease the energy consumption. In this review, an overview of the switching character and system signature harnessed in three archetypal MM systems is rendered, along with an integrated application survey for developing in‐sensor and in‐memory computing, viz., brain‐inspired or analogue computing, physical unclonable functions, and random number generators. The recent progress in theoretical studies that reveal the structural origin of the fast‐switching ability of the MM system is further summarized.

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Section: Outlooks Challenges and Opportunitiesmentioning

confidence: 99%

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Go,

Lim,

Lee

et al. 2024

Small Science

View full text Add to dashboard Cite

show abstract

“…The PHL of the memristor has already been observed in the experiments using different materials. Binary oxides, perovskite oxides, complex molecular materials, mixtures containing inorganic nanoparticles, and organic-inorganic interfaces are contemplated as the best suitable candidate for the switching layer of the memristor [29][30][31][32][33][34][35][36]. The fabrication of the sandwiched layer is considered the core of the memristor.…”

Section: Introductionmentioning

confidence: 99%

Phenomenological modeling of memristor fabricated through screen printing based on the structure of Ag/Polymer/Cu

Zafar¹,

Awais²,

Shehzad³

et al. 2023

Preprint

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The attributes of a memristor such as non-volatile, simple structure, no leakage current, and fast switching speed have unfolded enormous opportunities for various analog and digital applications. It is imperative to develop an accurate physical model of the memristor in order to design digital applications. The hitherto published memristor modeling approaches do not match with the practical memristor dynamics. A new model is developed by considering Schottky contact at the metal-insulator-metal (MIM) interfaces. In this research work, a novel memristor is also fabricated to validate the proposed model. A bead polymer based on methyl methacrylate and n-butyl methacrylate (MMBM) is firstly used to explore the resistive switching properties of the device. A cost-effective screen printing technique is first demonstrated to deposit the resistive switching layer for the fabrication of the memristor. The resistive switching behavior is observed in the sandwiched layer with a silver (Ag) as a top electrode and copper (Cu) as a bottom electrode. Surface morphology and electrical characterizations of the fabricated device are carried out by scanning electron microscope and 2 probe resistivity measurement. The results validate the formation of the Schottky barriers at the MIM interfaces of the fabricated device. The proposed model is compared with the device described in this paper having an error of 0.7609 (in terms of the relative root-meansquare error). Moreover, the NOR logic gate is simulated for the circuit simulation of the proposed model. This will pave a new path for the digital design applications based on the memristor.

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“…Standard WRITE/READ operations in ReRAM memories have been normally performed using voltage-based schemes [15], [16]. For instance, a single voltage pulse of a fixed duration is used for the WRITE operations (SET and RESET).…”

Section: Introductionmentioning

confidence: 99%

Effective Current-Driven Memory Operations for Low-Power ReRAM Applications

et al. 2023

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Resistive switching (RS) devices are electronic components which exhibit a resistive state that can be adjusted to different nonvolatile levels via electrical stressing, fueling the development of future resistive memories (ReRAM) and enabling innovative solutions for several applications. Most works so far have used voltage-based driving schemes for both WRITE and READ operations. However, results from current-driven WRITE operations have shown high uniformity in switching performance, and thus constitute a valid alternative to consider, but current-driven READ operations have rarely been explored. In this context, here we tested a current-based READ/WRITE memory driving scheme on commercial self-directed channel (SDC) devices, while operating constantly at low current levels between tenths of nA and 1.5 uA. We propose a novel method to carry out efficient READ operations exploiting the transient response of the voltage on the current-driven ReRAM memory cells. For READ operations performed at 100 nA, we calculated the cumulative probability distribution of the standard deviation of the measured voltage (σ V ) on the devices and we observed a ratio σ V−HRS /σ V−LRS ∼ 10×. Moreover, the HRS and LRS states were distinguishable in all the tested devices with less than 0.5% error. Finally, the calculated energy consumption (E SET ≈ 10 nJ, E RESET ≈ 30 nJ, and E READ between 80-400 pJ) was competitive even when the duration of the READ/WRITE current pulses was suboptimal in the millisecond range. Therefore, the presented results validate the promising characteristics and the power-efficiency of the proposed READ method for current-driven ReRAM circuits and applications.

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Custom measurement system for memristor characterisation

Cited by 8 publications

References 6 publications

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Phenomenological modeling of memristor fabricated through screen printing based on the structure of Ag/Polymer/Cu

Effective Current-Driven Memory Operations for Low-Power ReRAM Applications

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