우경석 교수

Abstract A computing scheme that can solve complex tasks is necessary as the big data field proliferates. Probabilistic computing (p-computing) paves the way to efficiently handle problems based on stochastic units called probabilistic bits (p-bits). This study proposes p-computing based on the threshold switching (TS) behavior of a Cu 0.1 Te 0.9 /HfO 2 /Pt (CTHP) diffusive memristor. The theoretical background of the p-computing resembling the Hopfield network structure is introduced to explain

Abstract Recent advances in physical reservoir computing, which is a type of temporal kernel, have made it possible to perform complicated timing-related tasks using a linear classifier. However, the fixed reservoir dynamics in previous studies have limited application fields. In this study, temporal kernel computing was implemented with a physical kernel that consisted of a W/HfO 2 /TiN memristor, a capacitor, and a resistor, in which the kernel dynamics could be arbitrarily controlled by chang

Herein, a true random number generator (TRNG) based on a Cu x Te 1− x diffusive memristor (DM) using its threshold switching (TS) behavior is reported. The intrinsic stochasticity of the TS behavior contributes to the randomness of the TRNG system. The switching behavior is discussed through field‐induced nucleation theory and surface diffusion dynamics. Demonstrating the performance of TRNG as a hardware security application, the DM‐based TRNG passes all 15 National Institute of Standards and T

Abstract A true random number generator (TRNG) based on the stochastic delay and relaxation times of the threshold switching (TS) behavior in a Pt/HfO 2 /TiN memristor is proposed. The stochasticities of this device are attributed to its electron trapping and detrapping processes. This electronic‐switching‐based memristor exhibits several advantages, such as low power consumption and high reliability. A new circuit is designed to improve the simplicity, miniaturization, and lifetime of TRNG. The

Abstract Information security and computing, two critical technological challenges for post-digital computation, pose opposing requirements – security (encryption) requires a source of unpredictability, while computing generally requires predictability. Each of these contrasting requirements presently necessitates distinct conventional Si-based hardware units with power-hungry overheads. This work demonstrates Cu 0.3 Te 0.7 /HfO 2 (‘CuTeHO’) ion-migration-driven memristors that satisfy the contr

Neuromorphic computing promises an energy-efficient alternative to traditional digital processors in handling data-heavy tasks, primarily driven by the development of both volatile (neuronal) and nonvolatile (synaptic) resistive switches or memristors. However, despite their energy efficiency, memristor-based technologies presently lack functional tunability, thus limiting their competitiveness with arbitrarily programmable (general purpose) digital computers. This work introduces a two-terminal

Abstract A true random‐number generator (TRNG) and a nonlinear feedback shift register (NFSR) are combined to create a new type of TRNG. This TRNG is based on the intrinsic stochasticity of threshold switching behavior in a Pt/HfO 2 /TiN memristor and an NFSR circuit. Considering the transition rate of the hopping process, the stochasticity of the delay time can be attributed to the phonon‐assisted hopping process. This novel TRNG passes all 15 National Institute of Standards and Technology rand

Memristor-based physical reservoir computing (RC) is a robust framework for processing complex spatiotemporal data parallelly. However, conventional memristor-based reservoirs cannot capture the spatial relationship between the time-varying inputs due to the specific mapping scheme assigning one input signal to one memristor conductance. Here, a physical "graph reservoir" is introduced using a metal cell at the diagonal-crossbar array (mCBA) with dynamic self-rectifying memristors. Input and inv

Abstract While digital computers rely on software-generated pseudo-random number generators, hardware-based true random number generators (TRNGs), which employ the natural physics of the underlying hardware, provide true stochasticity, and power and area efficiency. Research into TRNGs has extensively relied on the unpredictability in phase transitions, but such phase transitions are difficult to control given their often abrupt and narrow parameter ranges (e.g., occurring in a small temperature

memristor system present different behaviors in agreement with its history-dependent conductance and allow the fine-tuning of the behavior of each reservoir unit. The heterogeneity in the reservoir units improves the pattern recognition performance in the heterogeneous memristor RC system with a similar physical structure.

Abstract Many big data have interconnected and dynamic graph structures growing over time. Analyzing these graphical data requires the hidden relationship between the nodes in the graphs to be identified, which has conventionally been achieved by finding the effective similarity. However, graphs are generally non‐Euclidean, which does not allow finding it. In this study, the non‐Euclidean graphs are mapped to a specific crossbar array (CBA) composed of self‐rectifying memristors and metal cells

Abstract Probabilistic computing can solve complex combinatorial optimization problems more efficiently than conventional deterministic computing. A probabilistic bit (p‐bit) with an n‐p‐n bistable resistor (biristor) is demonstrated for probabilistic computing. It is fabricated on an 8‐inch wafer with complementary metal–oxide–semiconductor (CMOS) compatible technologies. Its stochastic behavior of threshold switching, which is based on the phenomenon of a single transistor latch, provides outp

selectivity. The OTS behavior was consistent with the modified Poole-Frenkel mechanism in the OFF state. In contrast, the similar GeSe film grown through the conventional ALD showed a low density and high vulnerability to oxidation, which prevented the OTS performance. The ALD method of GeSe films introduced here will contribute to the fabrication of a three-dimensionally integrated memory as a selector device for preventing sneak current.

Chalcogenide materials have been regarded as strong candidates for both resistor and selector elements in passive crossbar arrays owing to their dual capabilities of undergoing threshold and resistance switching. This work describes the bipolar resistive switching (BRS) of amorphous GeSe thin films, which used to show Ovonic threshold switching (OTS) behavior. The behavior of this new functionality of the material follows filament-based resistance switching when Ti and TiN are adopted as the top

Since the early 2000s, the impending end of Moore's scaling, as the physical limits to shrinking transistors have been approached, has fueled interest in improving the functionality and efficiency of integrated circuits by employing memristors or two-terminal resistive switches. Formation (or avoidance) of localized conducting channels in many memristors, often called "filaments", has been established as the basis for their operation. While we understand some qualitative aspects of the physical