Unpredictable bits generation based on RRAM parallel configuration

In this letter a cell with the parallel combination of two TiN/Ti/HfO2/W resistive random access memory (RRAM) devices is studied for the generation of unpredictable bits. Measurements confirm that a simultaneous parallel SET operation in which one of the two RRAMs switches to the low resistance state (LRS) is an unpredictable process showing random properties for different sets of cells. Furthermore, given a device pair, the same device switches during subsequent write operations. The proposed cell is also analyzed under different current compliances and pulse widths with the same persistent behavior being observed. The features of the proposed cell, which provide data obfuscation without compromising reliability, pave the way for its application in Physical Unclonable Functions (PUFs) for hardware security purposes.Peer ReviewedPostprint (author's final draft

RRAM Based Random Bit Generation for Hardware Security Applications

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Resistive random access memories (RRAMs) have arisen as a competitive candidate for non-volatile memories due to their scalability, simple structure, fast switching speed and compatibility with conventional back-end processes. The stochastic switching mechanism and intrinsic variability of RRAMs still poses challenges that must be overcome prior to their massive memory commercialization. However, these very same features open a wide range of potential applications for these devices in hardware security. In this context, this work proposes the generation of a random bit by means of simultaneous write operation of two parallel cells so that only one of them unpredictably switches its state. Electrical simulations confirm the strong stochastic behavior and stability of the proposed primitive. Exploiting this fact, a Physical Unclonable Function (PUF) like primitive is implemented based on modified 1 transistor - 1 resistor (1T1R) array structure.Peer ReviewedPostprint (published version

Low-power emerging memristive designs towards secure hardware systems for applications in internet of things

Emerging memristive devices offer enormous advantages for applications such as non-volatile memories and in-memory computing (IMC), but there is a rising interest in using memristive technologies for security applications in the era of internet of things (IoT). In this review article, for achieving secure hardware systems in IoT, low-power design techniques based on emerging memristive technology for hardware security primitives/systems are presented. By reviewing the state-of-the-art in three highlighted memristive application areas, i.e. memristive non-volatile memory, memristive reconfigurable logic computing and memristive artificial intelligent computing, their application-level impacts on the novel implementations of secret key generation, crypto functions and machine learning attacks are explored, respectively. For the low-power security applications in IoT, it is essential to understand how to best realize cryptographic circuitry using memristive circuitries, and to assess the implications of memristive crypto implementations on security and to develop novel computing paradigms that will enhance their security. This review article aims to help researchers to explore security solutions, to analyze new possible threats and to develop corresponding protections for the secure hardware systems based on low-cost memristive circuit designs

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing

Statistical Learning in Chip (SLIC) (Invited Paper)

Abstract-Despite best efforts, integrated systems are "born" (manufactured) with a unique 'personality' that stems from our inability to precisely fabricate their underlying circuits, and create software a priori for controlling the resulting uncertainty. It is possible to use sophisticated test methods to identify the bestperforming systems but this would result in unacceptable yields and correspondingly high costs. The system personality is further shaped by its environment (e.g., temperature, noise and supply voltage) and usage (i.e., the frequency and type of applications executed), and since both can fluctuate over time, so can the system's personality. Systems also "grow old" and degrade due to various wear-out mechanisms (e.g., negative-bias temperature instability), and unexpectedly due to various early-life failure sources. These "nature and nurture" influences make it extremely difficult to design a system that will operate optimally for all possible personalities. To address this challenge, we propose to develop statistical learning in-chip (SLIC). SLIC is a holistic approach to integrated system design based on continuously learning key personality traits on-line, for selfevolving a system to a state that optimizes performance hierarchically across the circuit, platform, and application levels. SLIC will not only optimize integrated-system performance but also reduce costs through yield enhancement since systems that would have before been deemed to have weak personalities (unreliable, faulty, etc.) can now be recovered through the use of SLIC