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

Short term plasticity. A neuromorphic perspective

Ramachandran H. Short term plasticity. A neuromorphic perspective. Bielefeld: Universität Bielefeld; 2018

Recent Advances in Thin Film Electronic Devices

This reprint is a collection of the papers from the Special Issue “Recent Advances in Thin Film Electronic Devices” in Micromachines. In this reprrint, 1 editorial and 11 original papers about recent advances in the research and development of thin film electronic devices are included. Specifically, three research fields are covered: device fundamentals (5 papers), fabrication processes (5 papers), and testing methods (1 paper). The experimental data, simulation results, and theoretical analysis presented in this reprint should benefit those researchers in flat panel displays, flat panel sensors, energy devices, memories, and so on

Resistive-RAM for Data Storage Applications.

Mainstream non-volatile memory technology, dominated by the floating gate transistor, has historically improved in density, performance and cost primarily by means of process scaling. This simple geometrical scaling now faces significant challenges due to constraints of electrostatics and reliability. Thus, novel non-transistor based memory paradigms are being widely explored. Among the various contenders for next generation storage technology, RRAM devices have got immense attention due to their high-speed, multilevel capability, scalability, simple structure, low voltage operation and high endurance. In this thesis, electrical and material characterization is carried out on a MIM device system and formation / annihilation of nanoscale filaments is shown to be the reason behind the resistance switching. The MIM system is optimized to include an in-cell resistor which is shown to improve device endurance and reduce stuck-at-one faults. For highest density, the devices were arranged in a crossbar geometry and vertically integrated on CMOS decoders to demonstrate the feasibility of practical data storage applications. Next, we show that these binary RRAM devices exhibit native stochastic nature of resistive switching. Even for a fixed voltage on the same device, the wait time associated with programming is not fixed and is random and broadly distributed. However, the probability of switching can be predicted and controlled by the programming pulse. These binary devices have been used to generate random bit-streams with predicable bias ratios in time and space domains. The ability to produce random bit-streams using binary resistive switching devices based on the native stochastic switching principle may potentially lead to novel non-von-Neumann computing paradigms. Further, sub-1nA operating current devices have been developed. This ultra-low current provides energy savings by minimizing programming, erase and read currents. Despite having such low currents, excellent retention, on/off ratio and endurance have been demonstrated. Finally a scalable approach to simple 3D stacking is discussed. By implementation of a vertical sidewall-based architecture, the number of critical lithography steps can be reduced. A vertical device structure based on a W / WOx / Pd material system is developed. This scalable architecture is well suited for development of analog memory and neuromorphic systems.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110461/1/sidgaba_1.pd