Big Data has an insatiable appetite for larger and better-performing memory. While current memory technologies continue to advance, the performance gaps in current memory and storage technology have motivated the exploration of emerging memory technologies capable of providing new functionalities. Ferroelectric memory is one such promising candidate which has recently experienced a revival after the discovery of ferroelectricity in hafnium dioxide (HfO 2 )—the dielectric of choice in advanced CMOS manufacturing. While the commercial viability of ferroelectric memory technology has made significant progress over the past decade, several challenges related to variation and reliability still stand as a barrier to large-scale commercial implementation. Here, we review some of the outstanding challenges of ferroelectric memory technology along with the recent materials and device innovations that are being considered to overcome them. Moreover, we aim to highlight these challenges as materials and device co-design problems that must be addressed through collaborative efforts that straddle the two disciplines. We identify and provide our perspective on some of the key challenges and opportunities for ferroelectric-based microelectronic technology.

Solving hard combinatorial optimization problems such as graph coloring efficiently continues to be an outstanding challenge for computing. Traditional digital computers typically entail an exponential increase in computing resources as the problem sizes increase. This makes larger problems of practical relevance intractable to compute, with subsequently adverse implications for a broad spectrum of ever-more relevant practical applications ranging from machine learning to electronic device automation (EDA). Here, we examine how analog coupled oscillators can enable area and energy-efficient methods to accelerate such problems. Further, we discuss how the implementation of such non-Boolean platforms can take advantage of emerging technologies such as scalable ferroelectrics.

Graph coloring is a NP-hard problem, and computing the solution on a digital computer entails an exponential increase in the computing resources (time, memory) with increasing problem size. This has motivated the search for alternate and more efficient non-Boolean approaches. Here, we experimentally demonstrate the solution to this problem using the phase dynamics of coupled oscillators. Using a 30-oscillator IC platform with reconfigurable all-to-all coupling and minimal post-processing, our approach achieves 98% accuracy in detecting (near-) optimal solutions within 1 color of the optimal solution in comparison to the 77% accuracy achieved with the heuristic Johnson algorithm. Additionally, we propose a new local search-based post-processing scheme to improve the quality of the coloring solution. Finally, using circuit simulations, we demonstrate the scalability and speed up (~ 100×) achievable with the above approach in larger graphs.

Spiking neural networks, inspired by biological neural systems, could process immense volumes of spatio-temporal data by representing them as spikes. Here, we propose to implement compact, scalable, energy-efficient spiking neurons based on the unique insulator-metal transition in Vanadium dioxide$(VO_{2})$which interact through memristive synapses, to emulate a Liquid State Machine (LSM). Further, we demonstrate the implementation of this recurrent neural network as a temporal auto-encoder, and adaptive channel equalizer for application in neuromorphic signal processing. Our approach provides a pathway to reduce component count$(50\ -100X)$and improve energy efficiency$(> 50X)$over conventional CMOS based implementations.

This paper reviews an analog circuit system of capacitively coupled relaxation oscillators whose time evolution can be used to solve the graph coloring problem. These oscillators consist of a series combination of an insulator-metal-transition (IMT) device and a resistance. Such circuits were also demonstrated experimentally using VO2(Vanadium Dioxide) as the phase transition material. The time evolution of circuit dynamics depend on eigenvectors of the adjacency matrix in the same way as is used by spectral algorithms for graph coloring. As such, a coupled network of such oscillators with piecewise linear dynamics have steady state phases which can be used to approximate the minimum vertex coloring of a graph.

In this paper we review recent work on computational paradigms involving coupled relaxation oscillators built using metal-insulator-transition (MIT) devices. Such oscillators made using MIT devices based on Vanadium-Dioxide thin films are very compact and can be realized in hardware. Networks of such oscillators have interesting phase and frequency dynamics which can be programmed to solve computationally hard problems.

Cross-point architectures are promising for designing dense memory arrays. However, sneak current paths in a cross-point array necessitates the use of non-linear selectors. In this paper, we analyze the potential of employing correlated materials exhibiting abrupt insulator-metal transitions as selectors to design cross-point memories based on magnetic tunnel junctions (MTJs). We analyze the properties of the correlated materials and co-design MTJs and the selector to optimize the energy efficiency and robustness of the memory array. Our analysis points to the need of a correlated material with a large ratio of insulator and metal resistivities along with appropriate critical currents for the phase transitions (the values of which depend on the absolute value of the resistivities). We discuss that the design constraints lead to a restriction on the range of the selector length, which is closely related to the oxide thickness of the MTJ. Comparison of the cross-point architecture with standard architecture shows the benefits in the former in terms of 7% larger sense margin and 5X higher integration density at iso-read stability. However, this comes at the cost of 2X lower write speed (due to two-cycle write) and 11%-19% increase in the read/write power (due to sneak current in the cross-point array).

We present a novel technique for optimizing the read operation of spin-transfer torque (STT) MRAMs by employing a correlated material in conjunction with a magnetic tunnel junction (MTJ). The design of the proposed memory cell is based on exploiting the orders-of-magnitude difference in the resistance of the two phases of the correlated material (CM) and triggering operation-driven phase transitions in the CM by judiciously co-optimizing devices and the memory cell. During read, the CM operates in the metallic and insulating phases when the MTJ is in the low resistance and high resistance states, respectively. This leads to superior distinguishability, read efficiency and stability. During write, the CM operates in the metallic phase, which minimizes the impact of the CM resistance on the write speed. Our analysis shows that CM amplifies the cell tunneling magneto-resistance from 107% (for the standard STT MRAM) to 1878% (for the proposed cell) leading to 68% higher sense margin. In addition, 45% enhancement in the read disturb margin and 36% reduction in the cell read power is achieved. At the same time, the write asymmetry associated with different state transitions is mildly mitigated, leading to 9% reduction in the write power. This comes at a negligible cost of 4% larger write time. We also discuss the layout implications of our technique and propose the sharing of the CM amongst multiple cells. As a result of the sharing, the proposed technique incurs no area penalty.

Harnessing the computational capabilities of dynamical systems has attracted the attention of scientists and engineers form varied technical disciplines over decades. The time evolution of coupled, non-linear synchronous oscillatory systems has led to active research in understanding their dynamical properties and exploring their applications in brain-inspired, neuromorphic computational models. In this paper we present the realization of coupled and scalable relaxation-oscillators utilizing the metal-insulator-metal transition of vanadium-dioxide (VO2) thin films. We demonstrate the potential use of such a system in pattern recognition, as one possible computational model using such a system.