Bi2O2Se-Based True Random Number Generator for Security Applications

Polarization-Switchable Electrochemistry of 2D Layered Bi2O2Se Bifunctional Microreactors by Ferroelectric Modulation

Ferroelectric catalysts are known for altering surface catalytic activities by changing the direction of their electric polarizations. This study demonstrates polarization-switchable electrochemistry using layered bismuth oxyselenide (L-Bi2O2Se) bifunctional microreactors through ferroelectric modulation. A selective-area ionic liquid gating is developed with precise control over the spatial distribution of the dipole orientation of L-Bi2O2Se. On-chip microreactors with upward polarization favor the oxygen evolution reaction, whereas those with downward polarization prefer the hydrogen evolution reaction. The microscopic origin behind polarization-switchable electrochemistry primarily stems from enhanced surface adsorption and reduced energy barriers for reactions, as examined by nanoscale scanning electrochemical cell microscopy. Integrating a pair of L-Bi2O2Se microreactors consisting of upward or downward polarizations demonstrates overall water splitting in a full-cell configuration based on a bifunctional catalyst. The ability to modulate surface polarizations on a single catalyst via ferroelectric polarization switching offers a pathway for designing catalysts for water splitting.

Leveraging volatile memristors in neuromorphic computing: from materials to system implementation

Inspired by the functions of biological neural networks, volatile memristors are essential for implementing neuromorphic computing. These devices enable large-scale and energy-efficient data processing by emulating neural functionalities through dynamic resistance changes. The threshold switching characteristics of volatile memristors, which are driven by various mechanisms in materials ranging from oxides to chalcogenides, make them versatile and suitable for neuromorphic computing systems. Understanding these mechanisms and selecting appropriate devices for specific applications are crucial for optimizing the performance. However, the existing literature lacks a comprehensive review of switching mechanisms, their compatibility with different applications, and a deeper exploration of the spatiotemporal processing capabilities and inherent stochasticity of volatile memristors. This review begins with a detailed analysis of the operational principles and material characteristics of volatile memristors. Their diverse applications are then explored, emphasizing their role in crossbar arrays, artificial receptors, and neurons. Furthermore, the potential of volatile memristors in artificial inference systems and reservoir computing is discussed, due to their spatiotemporal processing capabilities. Hardware security applications and probabilistic computing are also examined, where the inherent stochasticity of the devices can improve the system robustness and adaptability. To conclude, the suitability of different switching mechanisms for various applications is evaluated, and future perspectives for the development and implementation of volatile memristors are presented. This review aims to fill the gaps in existing research and highlight the potential of volatile memristors to drive innovation in neuromorphic computing, paving the way for more efficient and powerful computational paradigms.

Ferroelectric Stochasticity in 2D CuInP2S6 and Its Application for True Random Number Generator

True random number generators (TRNGs), which create cryptographically secure random bitstreams, hold great promise in addressing security concerns regarding hardware, communication, and authentication in the Internet of Things (IoT) realm. Recently, TRNGs based on nanoscale materials have gained considerable attention for avoiding conventional and predictable hardware circuitry designs that can be vulnerable to machine learning (ML) attacks. In this article, a low-power and low-cost TRNG developed by exploiting stochastic ferroelectric polarization switching in 2D ferroelectric CuInP2S6 (CIPS)-based capacitive structures, is reported. The stochasticity arises from the probabilistic switching of independent electrical dipoles. The TRNG exhibits enhanced stochastic variability with near-ideal entropy, uniformity, uniqueness, Hamming distance, and independence from autocorrelation variations. Its unclonability is systematically examined using device-to-device variations. The generated cryptographic bitstreams pass the National Institute of Standards and Technology (NIST) randomness tests. This nanoscale CIPS-based TRNG is circuit-integrable and exhibits potential for hardware security in edge devices with advanced data encryption.

Nonvolatile Modulation of Bi2O2Se/Pb(Zr,Ti)O3 Heteroepitaxy

The pursuit of high-performance electronic devices has driven the research focus toward 2D semiconductors with high electron mobility and suitable band gaps. Previous studies have demonstrated that quasi-2D Bi2O2Se (BOSe) has remarkable physical properties and is a promising candidate for further exploration. Building upon this foundation, the present work introduces a novel concept for achieving nonvolatile and reversible control of BOSe's electronic properties. The approach involves the epitaxial integration of a ferroelectric PbZr0.2Ti0.8O3 (PZT) layer to modify BOSe's band alignment. Within the BOSe/PZT heteroepitaxy, through two opposite ferroelectric polarization states of the PZT layer, we can tune the Fermi level in the BOSe layer. Consequently, this controlled modulation of the electronic structure provides a pathway to manipulate the electrical properties of the BOSe layer and the corresponding devices.

Bi2O2Se-based CBRAM integrated artificial synapse

Integrating two-dimensional (2D) semiconducting materials into memristor structures has paved the way for emerging 2D materials to be employed in a vast field of memory applications. Bismuth oxyselenide (Bi2O2Se), a 2D material with high electron mobility, has attracted significant research interest owing to its great potential in various fields of advanced applications. Here, we explore the out-of-plane intrinsic switching behavior of few-layered Bi2O2Se via a cross point device for application in conductive bridge random access memory (CBRAM) and artificial synapses for neuromorphic computing. Via state-of-the-art methods, CVD-grown Bi2O2Se nanoplate is applied as a switching material (SM) in an Al/Cu/Bi2O2Se/Pd CBRAM structure. The device exhibits ∼90 consecutive DC cycles with a tight distribution of the SET/RESET voltages under a compliance current (CC) of 1 mA, a retention of over 10 ks, and multilevel switching characteristics showing four distinct states at Vread values of 0.1, 0.2, 0.25, and 0.3 V. Moreover, an artificial synapse is realized with potentiation and depression by modulating the conductance. The switching mechanism is explained via Cu migration through Bi2O2Se based on HRTEM analysis. The present structure shows potential for future integrated memory applications.

Bi2O2Se-Based Bimode Noise Generator for the Application of Generative Adversarial Networks

In the emerging technology, the generative aversive networks (GANs), randomness, and unpredictability of inputting noises are the keys to the uniqueness, diversity, robustness, and security of the generated images. Compared with deterministic software-based noise generation, hardware-based noise generation introduces physical entropy sources, such as electronic and photonic noises, to add unpredictability. In this study, bimode Bi2O2Se-based noise generators have been demonstrated for the application of GANs. Harnessing its ultrahigh carrier mobility, excellent air stability, marvelous optoelectronic performance, as well as the unique surface resistive switching effect and defect locations in the energy diagram, Bi2O2Se provides a good material platform to easily integrate with multiple device architectures for generating noises in different physical sources. The noise of the black current mode in a photodetector architecture and the random telegraph noise in a memristor mode were measured, characterized, compared, and analyzed. A method of Markov chain equipped with K-means clustering was carried out to calculate the discrete noise states and the transition probability matrix between them. To evaluate the generated properties of the GANs based on the hardware noise source, the inception score and Fréchet inception distance were evaluated.

Attack Resilient True Random Number Generators Using Ferroelectric-Enhanced Stochasticity in 2D Transistor

By harnessing the physically unclonable properties, true random number generators (TRNGs) offer significant promises to alleviate security concerns by generating random bitstreams that are cryptographically secured. However, fundamental challenges remain as conventional hardware often requires complex circuitry design, showing a predictable pattern that is susceptible to machine learning attacks. Here, a low-power self-corrected TRNG is presented by exploiting the stochastic ferroelectric switching and charge trapping in molybdenum disulfide (MoS2 ) ferroelectric field-effect transistors (Fe-FET) based on hafnium oxide complex. The proposed TRNG exhibits enhanced stochastic variability with near-ideal entropy of ≈1.0, Hamming distance of ≈50%, independent autocorrelation function, and reliable endurance cycle against temperature variations. Furthermore, its unpredictable feature is systematically examined by machine learning attacks, namely the predictive regression model and the long-short-term-memory (LSTM) approach, where nondeterministic predictions can be concluded. Moreover, the generated cryptographic keys from the circuitry successfully pass the National Institute of Standards and Technology (NIST) 800-20 statistical test suite. The potential of integrating ferroelectric and 2D materials is highlighted for advanced data encryption, offering a novel alternative to generate truly random numbers.

Enthalpy and entropy synergistic regulation-based programmable DNA motifs for biosensing and information encryption

Deoxyribonucleic acid (DNA) provides a collection of intelligent tools for the development of information cryptography and biosensors. However, most conventional DNA regulation strategies rely solely on enthalpy regulation, which suffers from unpredictable stimuli-responsive performance and unsatisfactory accuracy due to relatively large energy fluctuations. Here, we report an enthalpy and entropy synergistic regulation-based pH-responsive A⁺/C DNA motif for programmable biosensing and information encryption. In the DNA motif, the variation in loop length alters entropic contribution, and the number of A⁺/C bases regulates enthalpy, which is verified through thermodynamic characterizations and analyses. On the basis of this straightforward strategy, the performances, such as pK_a, of the DNA motif can be precisely and predictably tuned. The DNA motifs are finally successfully applied for glucose biosensing and crypto-steganography systems, highlighting their potential in the field of biosensing and information encryption.

Hardware and Information Security Primitives Based on 2D Materials and Devices

Hardware security is a major concern for the entire semiconductor ecosystem that accounts for billions of dollars in annual losses. Similarly, information security is a critical need for the rapidly proliferating edge devices that continuously collect and communicate a massive volume of data. While silicon-based complementary metal-oxide-semiconductor technology offers security solutions, these are largely inadequate, inefficient, and often inconclusive, as well as resource intensive in time, energy, and cost, leading to tremendous room for innovation in this field. Furthermore, silicon-based security primitives have shown vulnerability to machine learning (ML) attacks. In recent years, 2D materials such as graphene and transition metal dichalcogenides have been intensely explored to mitigate these security challenges. In this review, 2D-materials-based hardware security solutions such as camouflaging, true random number generation, watermarking, anticounterfeiting, physically unclonable functions, and logic locking of integrated circuits (ICs) are summarized with accompanying discussion on their reliability and resilience to ML attacks. In addition, the role of native defects in 2D materials in developing high entropy hardware security primitives is also examined. Finally, the existing challenges for 2D materials, which must be overcome for large-scale deployment of 2D ICs to meet the security needs of the semiconductor industry, are discussed.

Memristor-Based Signal Processing for Compressed Sensing

With the rapid progress of artificial intelligence, various perception networks were constructed to enable Internet of Things (IoT) applications, thereby imposing formidable challenges to communication bandwidth and information security. Memristors, which exhibit powerful analog computing capabilities, emerged as a promising solution expected to address these challenges by enabling the development of the next-generation high-speed digital compressed sensing (CS) technologies for edge computing. However, the mechanisms and fundamental properties of memristors for achieving CS remain unclear, and the underlying principles for selecting different implementation methods based on various application scenarios have yet to be elucidated. A comprehensive overview of memristor-based CS techniques is currently lacking. In this article, we systematically presented CS requirements on device performance and hardware implementation. The relevant models were analyzed and discussed from the mechanism level to elaborate the memristor CS system scientifically. In addition, the method of deploying CS hardware using the powerful signal processing capabilities and unique performance of memristors was further reviewed. Subsequently, the potential of memristors in all-in-one compression and encryption was anticipated. Finally, existing challenges and future outlooks for memristor-based CS systems were discussed.

Reliable Memristor Based on Ultrathin Native Silicon Oxide

Memristors based on two-dimensional (2D) materials can exhibit great scalability and ultralow power consumption, yet the structural and thickness inhomogeneity of ultrathin electrolytes lowers the production yield and reliability of devices. Here, we report that the self-limiting amorphous SiO_x (∼2.7 nm) provides a perfect atomically thin electrolyte with high uniformity, featuring a record high production yield. With the guidance of physical modeling, we reveal that the atomic thickness of SiO_x enables anomalous resistive switching with a transition to an analog quasi-reset mode, where the filament stability can be further enhanced using Ag-Au nanocomposite electrodes. Such a picojoule memristor shows record low switching variabilities (C2C and D2D variation down to 1.1 and 2.6%, respectively), good retention at a few microsiemens, and high conductance-updating linearity, constituting key metrics for analog neural networks. In addition, the stable high-resistance state is found to be an excellent source for true random numbers of Gaussian distribution. This work opens up opportunities in mass production of Si-compatible memristors for ultradense neuromorphic and security hardware.