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As we approach the end of the silicon road map, alternative computing models that can solve at-scale problems in the data-centric world are becoming important. This is accompanied by the realization that binary abstraction and Boolean logic, which have been the foundations of modern computing revolution, fall short of the desired performance and power efficiency. In particular, hard computing problems relevant to pattern matching, image and signal processing, optimizations, and neuromorphic applications require alternative approaches. In this paper, we review recent advances in oscillatory dynamical system-based models of computing and their implementations. We show that simple configurations of oscillators connected using simple electrical circuits can result in interesting phase and frequency dynamics of such coupled oscillatory systems. Such networks can be controlled, programmed, and observed to solve computationally hard problems. Although our discussion in this paper is limited to insulator-to-metal transition devices and spin-torque oscillators, the general philosophy of such a computing paradigm of “let physics do the computing” can be translated to other mediums as well, including micromechanical and optical systems. We present an overview of the mathematical treatments necessary to understand the time evolution of these systems and highlight the recent experimental results in this area that suggest the potential of such computational models.

In recent years, several designs that use in-memory processing to accelerate machine-learning inference problems have been proposed. Such designs are also a perfect fit for discrete, dynamic, and distributed systems that can solve large-dimensional optimization problems using iterative algorithms. For in-memory computations, ferroelectric field-effect transistors (FerroFETs) owing to their compact area and distinguishable multiple states offer promising possibilities. We present a distributed architecture that uses FerroFET memory and implements in-memory processing to solve a template problem of least squares minimization. Through this architecture, we demonstrate an improvement of $21 \times $ in energy efficiency and $3 \times $ in compute time compared to a static random access memory (SRAM)-based processing-in-memory (PIM) architecture.

We fabricate, characterize, and establish the critical design criteria of Hf0.5Zr0.5O2 (HZO)-based ferroelectric field effect transistor (FeFET) for nonvolatile memory application. We quantify ${V}_{\textsf {TH}}$ shift from electron (hole) trapping in the vicinity of ferroelectric (FE)/interlayer (IL) interface, induced by erase (program) pulse, and ${V}_{\textsf {TH}}$ shift from polarization switching to determine true memory window (MW). The devices exhibit extrapolated retention up to 10 years at 85 °C and endurance up to $5\times 10^{6}$ cycles initiated by the IL breakdown. Endurance up to 1012 cycles of partial polarization switching is shown in metal–FE–metal capacitor, in the absence of IL. A comprehensive metal–FE–insulator–semiconductor FeFET model is developed to quantify the electric field distribution in the gate-stack, and an IL design guideline is established to markedly enhance MW, retention characteristics, and cycling endurance.

While Boolean logic has been the backbone of digital information processing, there exist classes of computationally hard problems wherein this paradigm is fundamentally inefficient. Vertex coloring of graphs, belonging to the class of combinatorial optimization, represents one such problem. It is well studied for its applications in data sciences, life sciences, social sciences and technology, and hence, motivates alternate, more efficient non-Boolean pathways towards its solution. Here we demonstrate a coupled relaxation oscillator based dynamical system that exploits insulator-metal transition in Vanadium Dioxide (VO

In this paper, we consider devices, circuits, and systems comprised of transistors with integrated ferroelectrics. Said structures are actively being considered by various semiconductor manufacturers as they can address a large and unique design space. Transistors with integrated ferroelectrics could (i) enable a better switch (i.e., offer steeper subthreshold swings), (ii) are CMOS compatible, (iii) have multiple operating modes (i.e., I-V characteristics can also enable compact, 1-transistor, non-volatile storage elements, as well as analog synaptic behavior), and (iv) have been experimentally demonstrated (i.e., with respect. to all of the aforementioned operating modes). These device level characteristics offer unique opportunities at the circuit, architectural, and system-level, and are considered here from device, circuit/architecture, and foundry-level perspectives.

High power consumption has significantly increased the cooling cost in high-performance computation stations and limited the operation time in portable systems powered by batteries. Traditional power reduction mechanisms have limited traction in the post-Dennard Scaling landscape. Emerging research on new computation devices and associated architectures has shown three trends with the potential to greatly mitigate current power limitations. The first is to employ steep-slope transistors to enable fundamentally more efficient operation at reduced supply voltage in conventional Boolean logic, reducing dynamic power. The second is to employ brain-inspired computation paradigms, directly embodying computation mechanisms inspired by the brains, which have shown potential in extremely efficient, if approximate, processing with silicon-neuron networks. The third is “let physics do the computation”, which focuses on using the intrinsic operation mechanism of devices (such as coupled oscillators) to do the approximate computation, instead of building complex circuits to carry out the same function. This paper first describes these three trends, and then proposes the use of the hybrid-phase-transition-FET (Hyper-FET), a device that could be configured as a steep-slope transistor, a spiking neuron cell, or an oscillator, as the device of choice for carrying these three trends forward. We discuss how a single class of device can be configured for these multiple use cases, and provide in-depth examination and analysis for a case study of building coupled-oscillator systems using Hyper-FETs for image processing. Performance benchmarking highlights the potential of significantly higher energy efficiency than dedicated CMOS accelerators at the same technology node.

Quantitative impedance mapping of the spatially inhomogeneous insulator-to-metal transition (IMT) in vanadium dioxide (VO2) is performed with a lateral resolution of 50 nm through near-field scanning microwave microscopy (SMM) at 16 GHz. SMM is used to measure spatially resolved electronic properties of the phase coexistence in an unstrained VO2 film during the electrically as well as thermally induced IMT. A quantitative impedance map of both the electrically driven filamentary conduction and the thermally induced bulk transition is established. This was modeled as a 2-D heterogeneous resistive network where the distribution function of the IMT temperature across the sample is captured. Applying the resistive network model for the electrically induced IMT case, we reproduce the filamentary nature of electronically induced IMT, which elucidates a cascading avalanche effect triggered by the local electric field across nanoscale insulating and metallic domains.

Artificial neural networks can harness stochasticity in multiple ways to enable a vast class of computationally powerful models. Electronic implementation of such stochastic networks is currently limited to addition of algorithmic noise to digital machines which is inherently inefficient; albeit recent efforts to harness physical noise in devices for stochasticity have shown promise. To succeed in fabricating electronic neuromorphic networks we need experimental evidence of devices with measurable and controllable stochasticity which is complemented with the development of reliable statistical models of such observed stochasticity. Current research literature has sparse evidence of the former and a complete lack of the latter. This motivates the current article where we demonstrate a stochastic neuron using an insulator-metal-transition (IMT) device, based on electrically induced phase-transition, in series with a tunable resistance. We show that an IMT neuron has dynamics similar to a piecewise linear FitzHugh-Nagumo (FHN) neuron and incorporates all characteristics of a spiking neuron in the device phenomena. We experimentally demonstrate spontaneous stochastic spiking along with electrically controllable firing probabilities using Vanadium Dioxide (VO$_2$) based IMT neurons which show a sigmoid-like transfer function. The stochastic spiking is explained by two noise sources - thermal noise and threshold fluctuations, which act as precursors of bifurcation. As such, the IMT neuron is modeled as an Ornstein-Uhlenbeck (OU) process with a fluctuating boundary resulting in transfer curves that closely match experiments. As one of the first comprehensive studies of a stochastic neuron hardware and its statistical properties, this article would enable efficient implementation of a large class of neuro-mimetic networks and algorithms., Added sectioning, Figure 6, Table 1, and Section II.E Updated abstract, discussion and corrected typos

In this paper we review recent work on novel computing paradigms using coupled oscillatory dynamical systems. We explore systems of relaxation oscillators based on linear state transitioning devices, which switch between two discrete states with hysteresis. By harnessing the dynamics of complex, connected systems we embrace the philosophy of "let physics do the computing" and demonstrate how complex phase and frequency dynamics of such systems can be controlled, programmed and observed to solve computationally hard problems. Although our discussion in this paper is limited to Insulator-to-Metallic (IMT) state transition devices, the general philosophy of such computing paradigms can be translated to other mediums including optical systems. We present the necessary mathematical treatments necessary to understand the time evolution of these systems and demonstrate through recent experimental results the potential of such computational primitives., Submitted to Journal of Nanophotonics for review

The spin-orbital interaction in heavy nonmagnetic metal/ferromagnetic metal bilayer systems has attracted great attention and exhibited promising potentials in magnetic logic devices, where the magnetization direction is controlled by passing an electric current. It is found that the spin-orbital interaction induces both an effective field and torque on the magnetization, which have been attributed to two different origins: the Rashba effect and the spin Hall effect. It requires quantitative analysis to distinguish the two mechanisms. Here we show sensitive spin-orbital effective field measurements up to 10 nm thick ferromagnetic layer and find the effective field rapidly diminishes with the increase of the ferromagnetic layer thickness. We further show that this effective field persists even with the insertion of a copper spacer. The nonlocal measurement suggests that the spin-orbital effective field does not rely on the heavy normal metal/ferromagnetic metal interface.