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JournalISSN: 0018-9200

IEEE Journal of Solid-state Circuits 

Institute of Electrical and Electronics Engineers
About: IEEE Journal of Solid-state Circuits is an academic journal published by Institute of Electrical and Electronics Engineers. The journal publishes majorly in the area(s): CMOS & Amplifier. It has an ISSN identifier of 0018-9200. Over the lifetime, 11089 publications have been published receiving 581213 citations. The journal is also known as: Journal of solid-state circuits & Institute of Electrical and Electronics Engineers journal of solid state circuits.


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Journal ArticleDOI
TL;DR: In this paper, the matching properties of the threshold voltage, substrate factor, and current factor of MOS transistors have been analyzed and measured, and the matching results have been verified by measurements and calculations on several basic circuits.
Abstract: The matching properties of the threshold voltage, substrate factor, and current factor of MOS transistors have been analyzed and measured. Improvements to the existing theory are given, as well as extensions for long-distance matching and rotation of devices. Matching parameters of several processes are compared. The matching results have been verified by measurements and calculations on several basic circuits. >

3,121 citations

Journal ArticleDOI
TL;DR: This paper considers the design, fabrication, and characterization of very small Mosfet switching devices suitable for digital integrated circuits, using dimensions of the order of 1 /spl mu/.
Abstract: This paper considers the design, fabrication, and characterization of very small Mosfet switching devices suitable for digital integrated circuits, using dimensions of the order of 1 /spl mu/. Scaling relationships are presented which show how a conventional MOSFET can be reduced in size. An improved small device structure is presented that uses ion implantation, to provide shallow source and drain regions and a nonuniform substrate doping profile. One-dimensional models are used to predict the substrate doping profile and the corresponding threshold voltage versus source voltage characteristic. A two-dimensional current transport model is used to predict the relative degree of short-channel effects for different device parameter combinations. Polysilicon-gate MOSFET's with channel lengths as short as 0.5 /spl mu/ were fabricated, and the device characteristics measured and compared with predicted values. The performance improvement expected from using these very small devices in highly miniaturized integrated circuits is projected.

3,008 citations

Journal ArticleDOI
TL;DR: In this paper, techniques for low power operation are presented which use the lowest possible supply voltage coupled with architectural, logic style, circuit, and technology optimizations to reduce power consumption in CMOS digital circuits while maintaining computational throughput.
Abstract: Motivated by emerging battery-operated applications that demand intensive computation in portable environments, techniques are investigated which reduce power consumption in CMOS digital circuits while maintaining computational throughput. Techniques for low-power operation are shown which use the lowest possible supply voltage coupled with architectural, logic style, circuit, and technology optimizations. An architecturally based scaling strategy is presented which indicates that the optimum voltage is much lower than that determined by other scaling considerations. This optimum is achieved by trading increased silicon area for reduced power consumption. >

2,690 citations

Journal ArticleDOI
TL;DR: In this paper, a general model is introduced which is capable of making accurate, quantitative predictions about the phase noise of different types of electrical oscillators by acknowledging the true periodically time-varying nature of all oscillators.
Abstract: A general model is introduced which is capable of making accurate, quantitative predictions about the phase noise of different types of electrical oscillators by acknowledging the true periodically time-varying nature of all oscillators. This new approach also elucidates several previously unknown design criteria for reducing close-in phase noise by identifying the mechanisms by which intrinsic device noise and external noise sources contribute to the total phase noise. In particular, it explains the details of how 1/f noise in a device upconverts into close-in phase noise and identifies methods to suppress this upconversion. The theory also naturally accommodates cyclostationary noise sources, leading to additional important design insights. The model reduces to previously available phase noise models as special cases. Excellent agreement among theory, simulations, and measurements is observed.

2,270 citations

Journal ArticleDOI
TL;DR: Eyeriss as mentioned in this paper is an accelerator for state-of-the-art deep convolutional neural networks (CNNs) that optimizes for the energy efficiency of the entire system, including the accelerator chip and off-chip DRAM, by reconfiguring the architecture.
Abstract: Eyeriss is an accelerator for state-of-the-art deep convolutional neural networks (CNNs). It optimizes for the energy efficiency of the entire system, including the accelerator chip and off-chip DRAM, for various CNN shapes by reconfiguring the architecture. CNNs are widely used in modern AI systems but also bring challenges on throughput and energy efficiency to the underlying hardware. This is because its computation requires a large amount of data, creating significant data movement from on-chip and off-chip that is more energy-consuming than computation. Minimizing data movement energy cost for any CNN shape, therefore, is the key to high throughput and energy efficiency. Eyeriss achieves these goals by using a proposed processing dataflow, called row stationary (RS), on a spatial architecture with 168 processing elements. RS dataflow reconfigures the computation mapping of a given shape, which optimizes energy efficiency by maximally reusing data locally to reduce expensive data movement, such as DRAM accesses. Compression and data gating are also applied to further improve energy efficiency. Eyeriss processes the convolutional layers at 35 frames/s and 0.0029 DRAM access/multiply and accumulation (MAC) for AlexNet at 278 mW (batch size $N = 4$ ), and 0.7 frames/s and 0.0035 DRAM access/MAC for VGG-16 at 236 mW ( $N = 3$ ).

2,165 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
2023427
2022358
2021335
2020314
2019309
2018309