Resistive switching devices are considered as one of the most promising candidates for the next generation memories and nonvolatile logic applications. In this paper, BiFeO3:Ti/BiFeO3 (BFTO/BFO) bilayer structures with optimized BFTO/BFO thickness ratio which show symmetric, bipolar, and nonvolatile resistive switching with good retention and endurance performance, are presented. The resistive switching mechanism is understood by a model of flexible top and bottom Schottky‐like barrier heights in the BFTO/BFO bilayer structures. The resistive switching at both positive and negative bias make it possible to use both polarities of reading bias to simultaneously program and store all 16 Boolean logic functions into a single cell of a BFTO/BFO bilayer structure in three logic cycles.
In this article, we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results.
Abstract-A switched-capacitor (SC) neuromorphic system for closed-loop neural coupling in 28 nm CMOS is presented, occupying 600 um by 600 um. It offers 128 input channels (i.e. presynaptic terminals), 8192 synapses and 64 output channels (i.e. neurons). Biologically realistic neuron and synapse dynamics are achieved via a faithful translation of the behavioural equations to SC circuits. As leakage currents significantly affect circuit behaviour at this technology node, dedicated compensation techniques are employed to achieve biological-realtime operation, with faithful reproduction of time constants of several 100 ms at room temperature. Power draw of the overall system is 1.9 mW.
A chemofluidic oscillator circuit that employs a hydrogel‐based chemofluidic transistor for chemical‐fluidic coupling is presented. It shows a period between 200 and 1000 s and alcohol concentrations oscillating between 2 wt% and 10 wt%. Because of the direct interaction with chemistry, chemofluidic transistors have the potential to facilitate labs‐on‐chips with enhanced functionality and scalability.
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