Seoul National University · Engineering
Professor Kyung Seok Woo's research lab specializes in next-generation neuromorphic and probabilistic computing using advanced resistive switching devices, particularly memristors based on HfO₂ and chalcogenide materials. The lab focuses on harnessing the intrinsic stochasticity of threshold switching in Cu-doped Te/HfO₂/Pt and similar structures to develop true random number generators (TRNGs) for hardware security, while also exploring their dual functionality in both secure cryptography and general-purpose computing. A key research direction involves achieving functional tunability in two-terminal bilayer memristors to emulate neuronal, synaptic, and hybrid behaviors, enabling energy-efficient, programmable computing architectures beyond conventional digital logic.
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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<sub>0.1</sub>Te<sub>0.9</sub>/HfO<sub>2</sub>/Pt (CTHP) diffusive memristor. The theoretical background of the p-computing resembling the Hopfield network structure is int
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
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<sub>0.3</sub>Te<sub>0.7</sub>/HfO<sub>2</sub> ('CuTeHO') ion-migration-driven memristors tha
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
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 window).
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
Multi-valued logic has been a significant focus of research in various fields with the advancement of information technology. One approach to realizing ternary logic is integrating of a threshold switching (TS) device with a transistor, but this method often entails a complex fabrication process. This work suggests a ternary gate-connected threshold switching thin-film transistor (TS-TFT) by serially connecting the TS device with a bottom-gate thin-film transistor (TFT). The fabrication process
True Random Number GeneratorsA true random number generator (TRNG) based on a CuxTe1-x diffusive memristor is proposed in article number 2100062 by Cheol Seong Hwang and co-workers. The intrinsic stochasticity of threshold switching behavior in the memristor contributes to the randomness of the TRNG system. As the importance of data security is increasing in the Internet of Things era, the adoption of diffusive memristors in TRNGs could play a crucial role.
Although memristor crossbars are a promising post-CMOS solution for computing, sneak currents and stochastic switching are two persistent challenges that impede their practical implementation. Here, we show how both issues can, in fact, be taken advantage for energy efficient computing. Using sneak paths to represent graphlets and stochasticity in hybrid volatile-nonvolatile memristors to mimic random walks, we perform graphlet decomposition and analysis, which are computationally hard problem w
Managing heat is a major challenge in modern silicon-based computers due to both large static and dynamic power dissipations. There is a growing perspective that heat can serve as an information carrier (instead of being treated as a useless by-product) in post-silicon devices, enabling new functions and on-chip energy recycling. In this review, we introduce how heat can be utilized as a degree of freedom in electronic devices, and how such devices may enable efficient computers.
Plasma-based interfacial treatments have previously enhanced the performance of filamentary-conductive resistive switching memories (RSMs). Still, strategies for improving bulk-conductive RSMs remain limited. While the bulk-conductive RSM has been explored for neuromorphic computing due to its gradual and analog switching behavior that allows for linear conductance change, it suffers from endurance degradation under repeated cycling. This study introduces a cyclic plasma treatment (CPT) method,
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