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In this paper, a new SPICE macromodel and CMOS emulator for memristors are proposed and verified to fit to the memristor's model equation very well in the entire range of memristor's resistance from the RESET state to the SET state. Compared with the memristor's model equation, average percentage errors in the new SPICE macromodel and in the 4-bit CMOS emulator are less than 0.5% and 0.9%, respectively. In addition, the CMOS emulator for memristors which can be implemented by a CMOS circuit will be very useful to design and verify various peripheral circuits for memristor applications particularly when the memristor fabrication process is not ready.
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http://dx.doi.org/10.1166/jnn.2012.4707 | DOI Listing |
Research (Wash D C)
January 2025
Key Laboratory for UV Light-Emitting Materials and Technology (Ministry of Education), College of Physics, Northeast Normal University, Changchun, China.
The optoelectronic memristor integrates the multifunctionalities of image sensing, storage, and processing, which has been considered as the leading candidate to construct novel neuromorphic visual system. In particular, memristive materials with all-optical modulation and complementary metal oxide semiconductor (CMOS) compatibility are highly desired for energy-efficient image perception. As a p-type oxide material, CuO exhibits outstanding theoretical photoelectric conversion efficiency and broadband photoresponse.
View Article and Find Full Text PDFFront Neurosci
December 2024
CIMAINA and Dipartimento di Fisica "A. Pontremoli", Università degli Studi di Milano, Milan, Italy.
The brain's ability to perform efficient and fault-tolerant data processing is strongly related to its peculiar interconnected adaptive architecture, based on redundant neural circuits interacting at different scales. By emulating the brain's processing and learning mechanisms, computing technologies strive to achieve higher levels of energy efficiency and computational performance. Although efforts to address neuromorphic solutions through hardware based on top-down CMOS-based technologies have obtained interesting results in terms of energetic efficiency improvement, the replication of brain's self-assembled and redundant architectures is not considered in the roadmaps of data processing electronics.
View Article and Find Full Text PDFOptical neural networks (ONNs) are custom optical circuits promising a breakthrough in low-power, parallelized, and high-speed hardware, for the growing demands of artificial intelligence applications. All-optical implementation of ONNs has proven burdensome chiefly due to the lack of optical devices that can emulate the neurons' non-linear activation function, thus forcing hybrid optical-electronic implementations. Moreover, ONNs suffer from a large footprint in comparison to their electronic (CMOS-based) counterparts.
View Article and Find Full Text PDFElectromagn Biol Med
October 2024
Division of Anesthesiology and Pain Medicine, Bizet Clinics, Paris, France.
Background: Oncological systemic treatments such as cytotoxic chemotherapy, radiation therapy or treatment with biological response modifiers can alter the quality of life (QoL) of cancer patients.The aim of this study is to assess the effects of cardiologic magnetic and optical stimulation (CMOS) on QoL in patients with advanced cancer receiving systemic treatment. For this purpose, we designed a non-invasive device that can reproduce and dynamically modulate stimulations of the same nature as the biological electromagnetic emissions specific to the body (cardiac).
View Article and Find Full Text PDFIEEE Trans Biomed Circuits Syst
August 2024
The realization of brain-scale spiking neural networks (SNNs) is impeded by power constraints and low integration density. To address these challenges, multi-core SNNs are utilized to emulate numerous neurons with high energy efficiency, where spike packets are routed through a network-on-chip (NoC). However, the information can be lost in the NoC under high spike traffic conditions, leading to performance degradation.
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