451 research outputs found
Recommended from our members
Charge Trap Transistors (CTT): Turning Logic Transistors into Embedded Non-Volatile Memory for Advanced High-k/Metal Gate CMOS Technologies
While need for embedded non-volatile memory (eNVM) in modern computing systems continues to grow rapidly, the options have been limited due to integration and scaling challenges as well as operational voltage incompatibilities. Introduced in this work is a unique multi-time programmable memory (MTPM) solution for advanced high-k/metal-gate (HKMG) CMOS technologies which turns as-fabricated standard logic transistors into eNVM elements, without the need for any process adders or additional masks. These logic transistors, when employed as eNVM elements, are dubbed “Charge Trap Transistors” (CTTs). The fundamental device physics, principles of operation, and technological breakthroughs required for employing logic transistors as eNVM are presented. Implementation of CTT eNVM in 32 nm, 22 nm, 14 nm, and 7 nm production technologies has been realized and demonstrated in this work. The emerging memory technology landscape and the space that the CTT technology occupies therein are examined.The motivation behind this work is to develop an eNVM technology that is completely process/mask-free, multi-time programmable, operable at low/logic-compatible voltages, scalable, and secure. The CTT technology satisfies all of the aforementioned criteria. CTTs offer a data retention lifetime of > 10 years at 125 �C and an operation temperature range of -55�-125� C. Hardware results demonstrate an endurance of > 10^4 program/erase cycles which is more than adequate for most embedded applications. Hardware security enhancement, on-chip reconfigurable encryption, firmware, BIOS, chip ID, redundancy, repair at wafer and module test and in the field, performance tailoring, and chip configuration are a few of the applications of CTT eNVM. Moreover, the CTT array in its native (unprogrammed) state measures very well as an entropy source for potential PUF (Physically Unclonable Function) applications such as identification, authentication, anti-counterfeiting, secure boot, and cryptographic IP. In addition to the numerous digital applications, CTTs can also be utilized as an analog memory for applications like neuromorphic computing for machine learning (ML) and artificial intelligence (AI)
DESTINY: A Comprehensive Tool with 3D and Multi-Level Cell Memory Modeling Capability
To enable the design of large capacity memory structures, novel memory technologies such as non-volatile memory (NVM) and novel fabrication approaches, e.g., 3D stacking and multi-level cell (MLC) design have been explored. The existing modeling tools, however, cover only a few memory technologies, technology nodes and fabrication approaches. We present DESTINY, a tool for modeling 2D/3D memories designed using SRAM, resistive RAM (ReRAM), spin transfer torque RAM (STT-RAM), phase change RAM (PCM) and embedded DRAM (eDRAM) and 2D memories designed using spin orbit torque RAM (SOT-RAM), domain wall memory (DWM) and Flash memory. In addition to single-level cell (SLC) designs for all of these memories, DESTINY also supports modeling MLC designs for NVMs. We have extensively validated DESTINY against commercial and research prototypes of these memories. DESTINY is very useful for performing design-space exploration across several dimensions, such as optimizing for a target (e.g., latency, area or energy-delay product) for a given memory technology, choosing the suitable memory technology or fabrication method (i.e., 2D v/s 3D) for a given optimization target, etc. We believe that DESTINY will boost studies of next-generation memory architectures used in systems ranging from mobile devices to extreme-scale supercomputers. The latest source-code of DESTINY is available from the following git repository: https://bitbucket.org/sparsh_mittal/destiny_v2
3D TCAD Study of the Implications of Channel Width and Interface States on FD-SOI Z2-FETs
3-D numerical technology computer-aided design simulations, based on experimental results, are performed to study the origin of the large Z 2 -FET dynamic random access memory (DRAM) memory cell-to-cell variability on fully depleted silicon-on-insulator (FD-SOI) technology. The body width, cross section shape, and the passivation-induced lateral and top interface state density impacts on the device dynamic memory operation are investigated. The width and body shape arise as marginal metrics not strongly inducing fluctuations in the device triggering conditions. However, the interface state (D it ) control, especially at the top of the ungated section, emerges as the main challenge since traps significantly increase the ON-voltage variability threatening the capacitor-less DRAM operation.H2020 REMINDER European (grant agreement No 687931) and Spanish National TEC2017-89800-R and PCIN-2015-146 projects are acknowledged for financial support
A Construction Kit for Efficient Low Power Neural Network Accelerator Designs
Implementing embedded neural network processing at the edge requires
efficient hardware acceleration that couples high computational performance
with low power consumption. Driven by the rapid evolution of network
architectures and their algorithmic features, accelerator designs are
constantly updated and improved. To evaluate and compare hardware design
choices, designers can refer to a myriad of accelerator implementations in the
literature. Surveys provide an overview of these works but are often limited to
system-level and benchmark-specific performance metrics, making it difficult to
quantitatively compare the individual effect of each utilized optimization
technique. This complicates the evaluation of optimizations for new accelerator
designs, slowing-down the research progress. This work provides a survey of
neural network accelerator optimization approaches that have been used in
recent works and reports their individual effects on edge processing
performance. It presents the list of optimizations and their quantitative
effects as a construction kit, allowing to assess the design choices for each
building block separately. Reported optimizations range from up to 10'000x
memory savings to 33x energy reductions, providing chip designers an overview
of design choices for implementing efficient low power neural network
accelerators
Homogeneous and heterogeneous MPSoC architectures with network-on-chip connectivity for low-power and real-time multimedia signal processing
Two multiprocessor system-on-chip (MPSoC) architectures are proposed and compared in the paper with reference to audio and video processing applications. One architecture exploits a homogeneous topology; it consists of 8 identical tiles, each made of a 32-bit RISC core enhanced by a 64-bit DSP coprocessor with local memory. The other MPSoC architecture exploits a heterogeneous-tile topology with on-chip distributed memory resources; the tiles act as application specific processors supporting a different class of algorithms. In both architectures, the multiple tiles are interconnected by a network-on-chip (NoC) infrastructure, through network interfaces and routers, which allows parallel operations of the multiple tiles. The functional performances and the implementation complexity of the NoC-based MPSoC architectures are assessed by synthesis results in submicron CMOS technology. Among the large set of supported algorithms, two case studies are considered: the real-time implementation of an H.264/MPEG AVC video codec and of a low-distortion digital audio amplifier. The heterogeneous architecture ensures a higher power efficiency and a smaller area occupation and is more suited for low-power multimedia processing, such as in mobile devices. The homogeneous scheme allows for a higher flexibility and easier system scalability and is more suited for general-purpose DSP tasks in power-supplied devices
Design of Low-Voltage Digital Building Blocks and ADCs for Energy-Efficient Systems
Increasing number of energy-limited applications continue to drive the demand for designing systems with high energy efficiency. This tutorial covers the main building blocks of a system implementation including digital logic, embedded memories, and analog-to-digital converters and describes the challenges and solutions to designing these blocks for low-voltage operation
A review of advances in pixel detectors for experiments with high rate and radiation
The Large Hadron Collider (LHC) experiments ATLAS and CMS have established
hybrid pixel detectors as the instrument of choice for particle tracking and
vertexing in high rate and radiation environments, as they operate close to the
LHC interaction points. With the High Luminosity-LHC upgrade now in sight, for
which the tracking detectors will be completely replaced, new generations of
pixel detectors are being devised. They have to address enormous challenges in
terms of data throughput and radiation levels, ionizing and non-ionizing, that
harm the sensing and readout parts of pixel detectors alike. Advances in
microelectronics and microprocessing technologies now enable large scale
detector designs with unprecedented performance in measurement precision (space
and time), radiation hard sensors and readout chips, hybridization techniques,
lightweight supports, and fully monolithic approaches to meet these challenges.
This paper reviews the world-wide effort on these developments.Comment: 84 pages with 46 figures. Review article.For submission to Rep. Prog.
Phy
Advances in Solid State Circuit Technologies
This book brings together contributions from experts in the fields to describe the current status of important topics in solid-state circuit technologies. It consists of 20 chapters which are grouped under the following categories: general information, circuits and devices, materials, and characterization techniques. These chapters have been written by renowned experts in the respective fields making this book valuable to the integrated circuits and materials science communities. It is intended for a diverse readership including electrical engineers and material scientists in the industry and academic institutions. Readers will be able to familiarize themselves with the latest technologies in the various fields
- …