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Probabilistic (p-) computing is a physics-based approach to addressing computational problems which are difficult to solve by conventional von Neumann computers. A key requirement for p-computing is the realization of fast, compact, and energy-efficient probabilistic bits. Stochastic magnetic tunnel junctions (MTJs) with low energy barriers, where the relative dwell time in each state is controlled by current, have been proposed as a candidate to implement p-bits. This approach presents challenges due to the need for precise control of a small energy barrier across large numbers of MTJs, and due to the need for an analog control signal. Here we demonstrate an alternative p-bit design based on perpendicular MTJs that uses the voltage-controlled magnetic anisotropy (VCMA) effect to create the random state of a p-bit on demand. The MTJs are stable (i.e. have large energy barriers) in the absence of voltage, and VCMA-induced dynamics are used to generate random numbers in less than 10 ns/bit. We then show a compact method of implementing p-bits by using VC-MTJs without a bias current. As a demonstration of the feasibility of the proposed p-bits and high quality of the generated random numbers, we solve up to 40 bit integer factorization problems using experimental bit-streams generated by VC-MTJs. Our proposal can impact the development of p-computers, both by supporting a fully spintronic implementation of a p-bit, and alternatively, by enabling true random number generation at low cost for ultralow-power and compact p-computers implemented in complementary metal-oxide semiconductor chips.

 

In the ‘Beyond Moore’s Law’ era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore’s Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.

 

Solving computationally hard problems using conventional computing architectures is often slow and energetically inefficient. Quantum computing may help with these challenges, but it is still in the early stages of development. A quantum-inspired alternative is to build domain-specific architectures with classical hardware. Here we report a sparse Ising machine that achieves massive parallelism where the flips per second—the key figure of merit—scales linearly with the number of probabilistic bits. Our sparse Ising machine architecture, prototyped on a field-programmable gate array, is up to six orders of magnitude faster than standard Gibbs sampling on a central processing unit, and offers 5–18 times improvements in sampling speed compared with approaches based on tensor processing units and graphics processing units. Our sparse Ising machine can reliably factor semi-primes up to 32 bits and it outperforms competition-winning Boolean satisfiability solvers in approximate optimization. Moreover, our architecture can find the correct ground state, even when inexact sampling is made with faster clocks. Our problem encoding and sparsification techniques could be applied to other classical and quantum Ising machines, and our architecture could potentially be scaled to 1,000,000 or more p-bits using analogue silicon or nanodevice technologies. 

With the fast growth of the number of electronic devices on the internet of things (IoT), hardware‐based security primitives such as physically unclonable functions (PUFs) have emerged to overcome the shortcomings of conventional software‐based cryptographic technology. Existing PUFs exploit manufacturing process variations in a semiconductor foundry technology. This results in a static challenge–response behavior, which can present a long‐term security risk. This study shows a reconfigurable PUF based on nanoscale magnetic tunnel junction (MTJ) arrays that uses stochastic dynamics induced by voltage‐controlled magnetic anisotropy (VCMA) for true random bit generation. A total of 100 PUF instances are implemented using 10 ns voltage pulses on a single chip with a 10 × 10 MTJ array. The unipolar nature of the VCMA mechanism is exploited to stabilize the MTJ state and eliminate bit errors during readout. All PUF instances show entropy close to one, inter‐Hamming distance close to 50%, and no bit errors in 10⁴repeated readout measurements.

 

Abstract There is accelerating interest in developing memory devices using antiferromagnetic (AFM) materials, motivated by the possibility for electrically controlling AFM order via spin-orbit torques, and its read-out via magnetoresistive effects. Recent studies have shown, however, that high current densities create non-magnetic contributions to resistive switching signals in AFM/heavy metal (AFM/HM) bilayers, complicating their interpretation. Here we introduce an experimental protocol to unambiguously distinguish current-induced magnetic and nonmagnetic switching signals in AFM/HM structures, and demonstrate it in IrMn 3 /Pt devices. A six-terminal double-cross device is constructed, with an IrMn 3 pillar placed on one cross. The differential voltage is measured between the two crosses with and without IrMn 3 after each switching attempt. For a wide range of current densities, reversible switching is observed only when write currents pass through the cross with the IrMn 3 pillar, eliminating any possibility of non-magnetic switching artifacts. Micromagnetic simulations support our findings, indicating a complex domain-mediated switching process. 

Current-induced spin-orbit torques (SOTs) are of interest for fast and energy-efficient manipulation of magnetic order in spintronic devices. To be deterministic, however, switching of perpendicularly magnetized materials by SOT requires a mechanism for in-plane symmetry breaking. Existing methods to do so involve the application of an in-plane bias magnetic field, or incorporation of in-plane structural asymmetry in the device, both of which can be difficult to implement in practical applications. Here, we report bias-field-free SOT switching in a single perpendicular CoTb layer with an engineered vertical composition gradient. The vertical structural inversion asymmetry induces strong intrinsic SOTs and a gradient-driven Dzyaloshinskii–Moriya interaction (g-DMI), which breaks the in-plane symmetry during the switching process. Micromagnetic simulations are in agreement with experimental results, and elucidate the role of g-DMI in the deterministic switching processes. This bias-field-free switching scheme for perpendicular ferrimagnets with g-DMI provides a strategy for efficient and compact SOT device design.