Nanoscale diffusive memristor crossbars as physical unclonable functions

Tunable ion energy barrier modulation through aliovalent halide doping for reliable and dynamic memristive neuromorphic systems

Memristive neuromorphic computing has emerged as a promising computing paradigm for the upcoming artificial intelligence era, offering low power consumption and high speed. However, its commercialization remains challenging due to reliability issues from stochastic ion movements. Here, we propose an innovative method to enhance the memristive uniformity and performance through aliovalent halide doping. By introducing fluorine concentration into dynamic TiO2-x memristors, we experimentally demonstrate reduced device variations, improved switching speeds, and enhanced switching windows. Atomistic simulations of amorphous TiO2-x reveal that fluoride ions attract oxygen vacancies, improving the reversible redistribution and uniformity. A number of migration barrier calculations statistically show that fluoride ions also reduce the migration energies of nearby oxygen vacancies, facilitating ionic diffusion and high-speed switching. The detailed Voronoi volume analysis further suggests design principles in terms of the migrating species' electrostatic repulsion and migration barriers. This work presents an innovative methodology for the fabrication of reliable memristor devices, contributing to the realization of hardware-based neuromorphic systems.

Tunable stochastic memristors for energy-efficient encryption and computing

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 Cu0.3Te0.7/HfO2 ('CuTeHO') ion-migration-driven memristors that satisfy the contrasting requirements. Under specific operating biases, CuTeHO memristors generate truly random and physically unclonable functions, while under other biases, they perform universal Boolean logic. Using these computing primitives, this work experimentally demonstrates a single system that performs cryptographic key generation, universal Boolean logic operations, and encryption/decryption. Circuit-based calculations reveal the energy and latency advantages of the CuTeHO memristors in these operations. This work illustrates the functional flexibility of memristors in implementing operations with varying component-level requirements.

All-silicon multidimensionally-encoded optical physical unclonable functions for integrated circuit anti-counterfeiting

Integrated circuit anti-counterfeiting based on optical physical unclonable functions (PUFs) plays a crucial role in guaranteeing secure identification and authentication for Internet of Things (IoT) devices. While considerable efforts have been devoted to exploring optical PUFs, two critical challenges remain: incompatibility with the complementary metal-oxide-semiconductor (CMOS) technology and limited information entropy. Here, we demonstrate all-silicon multidimensionally-encoded optical PUFs fabricated by integrating silicon (Si) metasurface and erbium-doped Si quantum dots (Er-Si QDs) with a CMOS-compatible procedure. Five in-situ optical responses have been manifested within a single pixel, rendering an ultrahigh information entropy of 2.32 bits/pixel. The position-dependent optical responses originate from the position-dependent radiation field and Purcell effect. Our evaluation highlights their potential in IoT security through advanced metrics like bit uniformity, similarity, intra- and inter-Hamming distance, false-acceptance and rejection rates, and encoding capacity. We finally demonstrate the implementation of efficient lightweight mutual authentication protocols for IoT applications by using the all-Si multidimensionally-encoded optical PUFs.

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.

Ultrafast Near-Ideal Phase-Change Memristive Physical Unclonable Functions Driven by Amorphous State Variations

There is an ever-increasing demand for next-generation devices that do not require passwords and are impervious to cloning. For traditional hardware security solutions in edge computing devices, inherent limitations are addressed by physical unclonable functions (PUF). However, realizing efficient roots of trust for resource constrained hardware remains extremely challenging, despite excellent demonstrations with conventional silicon circuits and archetypal oxide memristor-based crossbars. An attractive, down-scalable approach to design efficient cryptographic hardware is to harness memristive materials with a large-degree-of-randomness in materials state variations, but this strategy is still not well understood. Here, the utilization of high-degree-of-randomness amorphous (A) state variations associated with different operating conditions via thermal fluctuation effects is demonstrated, as well as an integrated framework for in memory computing and next generation security primitives, viz., APUF, for achieving secure key generation and device authentication. Near ideal uniformity and uniqueness without additional initial writing overheads in weak memristive A-PUF is achieved. In-memory computing empowers a strong exclusive OR (XOR-) and-repeat A PUF construction to avoid machine learning attacks, while rapid crystallization processes enable large-sized-key reconfigurability. These findings pave the way for achieving a broadly applicable security primitive for enhancing antipiracy of integrated systems and product authentication in supply chains.

Probabilistic computing using Cu0.1Te0.9/HfO2/Pt diffusive memristors

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_0.1Te_0.9/HfO₂/Pt (CTHP) diffusive memristor. The theoretical background of the p-computing resembling the Hopfield network structure is introduced to explain the p-computing system. P-bits are realized by the stochastic TS behavior of CTHP diffusive memristors, and they are connected to form the p-computing network. The memristor-based p-bit is likely to be ‘0’ and ‘1’, of which probability is controlled by an input voltage. The memristor-based p-computing enables all 16 Boolean logic operations in both forward and inverted operations, showing the possibility of expanding its uses for complex operations, such as full adder and factorization.

Designing a computing scheme to solve complex tasks as the big data field proliferates remains a challenge. Here, the authors present a probabilistic bit generation hardware built using the random nature of Cu_xTe_1−x/HfO₂/Pt memristors capable of performing logic gates with invertible mode, showing the expandability to complex logic circuits.

All-in-one, bio-inspired, and low-power crypto engines for near-sensor security based on two-dimensional memtransistors

In the emerging era of the internet of things (IoT), ubiquitous sensors continuously collect, consume, store, and communicate a huge volume of information which is becoming increasingly vulnerable to theft and misuse. Modern software cryptosystems require extensive computational infrastructure for implementing ciphering algorithms, making them difficult to be adopted by IoT edge sensors that operate with limited hardware resources and at low energy budgets. Here we propose and experimentally demonstrate an “all-in-one” 8 × 8 array of robust, low-power, and bio-inspired crypto engines monolithically integrated with IoT edge sensors based on two-dimensional (2D) memtransistors. Each engine comprises five 2D memtransistors to accomplish sensing and encoding functionalities. The ciphered information is shown to be secure from an eavesdropper with finite resources and access to deep neural networks. Our hardware platform consists of a total of 320 fully integrated monolayer MoS₂-based memtransistors and consumes energy in the range of hundreds of picojoules and offers near-sensor security.

Internet of things (IoT) sensors can collect, store and communicate large volumes of information, which require effective security measures. Here, the authors report the realization of low-power edge sensors based on photosensitive and programmable 2D memtransistors, integrating sensing, storage and encryption functionalities.

Conductive Bridge Random Access Memory (CBRAM): Challenges and Opportunities for Memory and Neuromorphic Computing Applications

Due to a rapid increase in the amount of data, there is a huge demand for the development of new memory technologies as well as emerging computing systems for high-density memory storage and efficient computing. As the conventional transistor-based storage devices and computing systems are approaching their scaling and technical limits, extensive research on emerging technologies is becoming more and more important. Among other emerging technologies, CBRAM offers excellent opportunities for future memory and neuromorphic computing applications. The principles of the CBRAM are explored in depth in this review, including the materials and issues associated with various materials, as well as the basic switching mechanisms. Furthermore, the opportunities that CBRAMs provide for memory and brain-inspired neuromorphic computing applications, as well as the challenges that CBRAMs confront in those applications, are thoroughly discussed. The emulation of biological synapses and neurons using CBRAM devices fabricated with various switching materials and device engineering and material innovation approaches are examined in depth.

Reconfigurable Stochastic neurons based on tin oxide/MoS2 hetero-memristors for simulated annealing and the Boltzmann machine

Neuromorphic hardware implementation of Boltzmann Machine using a network of stochastic neurons can allow non-deterministic polynomial-time (NP) hard combinatorial optimization problems to be efficiently solved. Efficient implementation of such Boltzmann Machine with simulated annealing desires the statistical parameters of the stochastic neurons to be dynamically tunable, however, there has been limited research on stochastic semiconductor devices with controllable statistical distributions. Here, we demonstrate a reconfigurable tin oxide (SnO_x)/molybdenum disulfide (MoS₂) heterogeneous memristive device that can realize tunable stochastic dynamics in its output sampling characteristics. The device can sample exponential-class sigmoidal distributions analogous to the Fermi-Dirac distribution of physical systems with quantitatively defined tunable "temperature" effect. A BM composed of these tunable stochastic neuron devices, which can enable simulated annealing with designed "cooling" strategies, is conducted to solve the MAX-SAT, a representative in NP-hard combinatorial optimization problems. Quantitative insights into the effect of different "cooling" strategies on improving the BM optimization process efficiency are also provided.

All-Inorganic Perovskite Nanorod Arrays with Spatially Randomly Distributed Lasing Modes for All-Photonic Cryptographic Primitives

The level of hardware or information security can be increased by applying physical unclonable functions (PUFs), which have a high complexity and unique nonreplicability and are based on random physical patterns generated by nature, to anticounterfeiting and encryption technologies. The preparation of PUFs should be as simple and convenient as possible, while maintaining the high complexity and stability of PUFs to ensure high reliability in use. In this study, an all-inorganic perovskite single-crystal array with a controllable morphology and a random size was prepared by a one-step recrystallization method in a solvent atmosphere to generate all-photonic cryptographic primitives. The nondeterministic size of the perovskite nanorods mainly arises from crystal growth in an indeterminate direction, producing a high entropy for the system. The cavity-size-dependent lasing emission behavior of perovskite single crystals was investigated as a preliminary exploration of the generation of all-photonic cryptographic primitives. The lasing-mode number was positively correlated with the length of the perovskite nanorods. Therefore, the prepared perovskite nanorod array with random sizes can be transformed into a quaternary cryptographic key array following encoding rules based on the lasing-mode number. Superior lasing stability was observed for the all-inorganic perovskite under continuous excitation, demonstrating the high reliability of this system.

Advanced Data Encryption ​using 2D Materials

Advanced data encryption requires the use of true random number generators (TRNGs) to produce unpredictable sequences of bits. TRNG circuits with high degree of randomness and low power consumption may be fabricated by using the random telegraph noise (RTN) current signals produced by polarized metal/insulator/metal (MIM) devices as entropy source. However, the RTN signals produced by MIM devices made of traditional insulators, i.e., transition metal oxides like HfO₂ and Al₂ O₃ , are not stable enough due to the formation and lateral expansion of defect clusters, resulting in undesired current fluctuations and the disappearance of the RTN effect. Here, the fabrication of highly stable TRNG circuits with low power consumption, high degree of randomness (even for a long string of 2²⁴  - 1 bits), and high throughput of 1 Mbit s^-1 by using MIM devices made of multilayer hexagonal boron nitride (h-BN) is shown. Their application is also demonstrated to produce one-time passwords, which is ideal for the internet-of-everything. The superior stability of the h-BN-based TRNG is related to the presence of few-atoms-wide defects embedded within the layered and crystalline structure of the h-BN stack, which produces a confinement effect that avoids their lateral expansion and results in stable operation.

Physically Unclonable Surfaces via Dewetting of Polymer Thin Films

From anti-counterfeiting to biotechnology applications, there is a strong demand for encoded surfaces with multiple security layers that are prepared by stochastic processes and are adaptable to deterministic fabrication approaches. Here, we present dewetting instabilities in nanoscopic (thickness <100 nm) polymer films as a form of physically unclonable function (PUF). The inherent randomness involved in the dewetting process presents a highly suitable platform for fabricating unclonable surfaces. The thermal annealing-induced dewetting of poly(2-vinyl pyridine) (P2VP) on polystyrene-grafted substrates enables fabrication of randomly positioned functional features that are separated at a microscopic length scale, a requirement set by optical authentication systems. At a first level, PUFs can be simply and readily verified via reflection of visible light. Area-specific electrostatic interactions between P2VP and citrate-stabilized gold nanoparticles allow for fabrication of plasmonic PUFs. The strong surface-enhanced Raman scattering by plasmonic nanoparticles together with incorporation of taggants facilitates a molecular vibration-based security layer. The patterning of P2VP films presents opportunities for fabricating hybrid security labels, which can be resolved through both stochastic and deterministic pathways. The adaptability to a broad range of nanoscale materials, simplicity, versatility, compatibility with conventional fabrication approaches, and high levels of stability offer key opportunities in encoding applications.

Controlled Growth of Fine Multifilaments in Polymer-Based Memristive Devices Via the Conduction Control

Solid polymer electrolyte (SPE) is one of the choices for many ionic devices, including organic transistors, ion batteries, memristors, and more. However, uncontrollable conductive filament formation seriously affects the performance of the device. In this paper, the PEDOT:PSS was doped to improve the electronic and ionic conductivity of amorphous polymer electrolyte poly(vinylpyrrolidone) (PVP), realizing the transition of the filaments growth from cathode to anode in atomic switch memristors. Based on the difference in ion and electron mobility and scanning electron microscope observation, the in situ reductions of metal ions inside the dielectric layer can effectively prevent the formation of uncontrollable filaments. The formation of uniformly distributed metal particles in the dielectric layer is similar to co-sputter doping technology, which enables the device to exhibit excellent performance, such as smaller set/reset bias distribution, endurance, and retention. Obviously, this innovative way improves the conductive mechanism of ionic devices.

Evolution map of the memristor: from pure capacitive state to resistive switching state

Memristors possess great application prospects in terabit nonvolatile storage devices, memory-in-logic algorithmic chips and bio-inspired artificial neural network systems. However, "what is the origin state of the memristor?" has remained an unanswered question for half a century. While many applications rely on the memristor, its origin state is becoming a fundamental issue. Herein, we reveal a new state, the pure capacitance state (PCS), which occurs before the memristor is triggered, and the origin state of the memristor can be verified in the memory cells through controlling the ambience parameters. Discovery of the PCS, a missing earlier stage of the memristor, completes the whole evolution map of the memristor from the very beginning to the final developed state.

Memristor in a Reservoir System-Experimental Evidence for High-Level Computing and Neuromorphic Behavior of PbI2

Lead halides in an asymmetric layered structure form memristive devices which are controlled by the electronic structure of the PbX₂|metal interface. In this paper, we explain the mechanism that stands behind the I- V pinched hysteresis loop of the device and shortly present its synaptic-like plasticity (spike-timing-dependent plasticity and spike-rate-dependent plasticity) and nonvolatile memory effects. This memristive element was incorporated into a reservoir system, in particular, the echo-state network with delayed feedback, which exhibits brain-like recurrent behavior and demonstrates metaplasticity as one of the available learning mechanisms. It can serve as a classification system that classifies input signals according to their amplitude.