International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

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Introduction To Electrodynamics

Traditional von Neumann computing systems involve separate processing and memory units. However, data movement is costly in terms of time and energy and this problem is aggravated by the recent explosive growth in highly data-centric applications related to artificial intelligence. This calls for a radical departure from the traditional systems and one such non-von Neumann computational approach is in-memory computing. Hereby certain computational tasks are performed in place in the memory itself by exploiting the physical attributes of the memory devices. Both charge-based and resistance-based memory devices are being explored for in-memory computing. In this Review, we provide a broad overview of the key computational primitives enabled by these memory devices as well as their applications spanning scientific computing, signal processing, optimization, machine learning, deep learning and stochastic computing. This Review provides an overview of memory devices and the key computational primitives for in-memory computing, and examines the possibilities of applying this computing approach to a wide range of applications.

Memory devices and applications for in-memory computing

Introduction to Nanotechnology

High-performance parallel computer architecture and systems have been improved at a phenomenal rate. In the meantime, VLSI computer-aided design (CAD) software for multibillion-transistor IC design has become increasingly complex and requires prohibitively high computational resources. Recent studies have shown that, numerous CAD problems, with their high computational complexity, can greatly benefit from the fast-increasing parallel computation capabilities. However, parallel programming imposes big challenges for CAD applications. Fully exploiting the computational power of emerging general-purpose and domain-specific multicore/many-core processor systems, calls for fundamental research and engineering practice across every stage of parallel CAD design, from algorithm exploration, programming models, design-time and run-time environment, to CAD applications, such as verification, optimization, and simulation. This journal special section will cover recent progress on parallel CAD research, including algorithm foundations, programming models, parallel architectural-specific optimization, and verification. More specifically, papers with in-depth and extensive coverage of the following topics will be considered, as well as other topics relevant to the design of parallel CAD algorithms and software tools. 1. Parallel algorithm design and specification for CAD applications 2. Parallel programming models and languages of particular use in CAD 3. Runtime support and performance optimization for CAD applications 4. Parallel architecture-specific design and optimization for CAD applications 5. Parallel program debugging and verification techniques particularly relevant for CAD The papers should be submitted via the Manuscript Central website and should adhere to standard ACM TODAES formatting requirements (http://todaes.acm.org/). The page count limit is 25.

Parallel CAD: Algorithm Design and Programming Special Section Call for Papers TODAES: ACM Transactions on Design Automation of Electronic Systems

One of the most critical challenges for today's and future data-intensive and big-data problems is data storage and analysis. This paper first highlights some challenges of the new born Big Data paradigm and shows that the increase of the data size has already surpassed the capabilities of today's computation architectures suffering from the limited bandwidth, programmability overhead, energy inefficiency, and limited scalability. Thereafter, the paper introduces a new memristor-based architecture for data-intensive applications. The potential of such an architecture in solving data-intensive problems is illustrated by showing its capability to increase the computation efficiency, solving the communication bottleneck, reducing the leakage currents, etc. Finally, the paper discusses why memristor technology is very suitable for the realization of such an architecture; using memristors to implement dual functions (storage and logic) is illustrated.

Memristor based computation-in-memory architecture for data-intensive applications

Memristor technology is a promising alternative to CMOS due to its high integration density, near-zero standby power, and ability to implement novel resistive computing. One of the major limitations of these architectures is the limited endurance of memristor devices, especially when a logic gate requires multiple steps/switching to execute the logic operations. To alleviate the endurance requirement and improve the performance, we present a novel logic design style, called scouting logic that executes any logic gate by only reading the memristor devices and without changing their states. Hence, no impact on the memristors' endurance. The proposed design is implemented using two styles (current and voltage based). To illustrate the performance of scouting logic based designs, the area, delay, and power consumption are analyzed and compared with state-ofthe- art. The results show that scouting logic improves the delay and power consumption by at least a factor of 2.3, while having similar or less area overhead. Finally, we discuss the potential applications and challenges of scouting logic.

Scouting Logic: A Novel Memristor-Based Logic Design for Resistive Computing

Memristor-based memory technology, also referred to as resistive RAM (RRAM), is one of the emerging memory technologies potentially to replace conventional semiconductor memories such as SRAM, DRAM, and flash. Existing research on such novel circuits focuses mainly on the integration between CMOS and non-CMOS, fabrication techniques, and reliability improvement. However, research on (manufacturing) test for yield and quality improvement is still in its infancy stage. This paper presents fault analysis and modeling for open defects based on electrical simulation, introduces fault models, and proposes test approaches for RRAMs. The fault analysis reveals that unique faults occur in addition to some conventional memory faults, and the detection of such unique faults cannot be guaranteed with just the application of traditional march tests. The paper also presents a new Design-for-Testability (DfT) concept to facilitate the detection of the unique faults. Two DfT schemes are developed by exploiting the access time duration and supply voltage level of the RRAM cells, and their simulation results show that the fault coverage can be increased with minor circuit modification. As the fault behavior may vary due to process variations, the DfT schemes are extended to be programmable to track the changes and further improve the fault/defect coverage.

Testing Open Defects in Memristor-Based Memories

As the CMOS technology is gradually scaling down to inherent physical device limits, significant challenges emerge related to scalability, leakage, reliability, etc. Alternative technologies are under research for next-generation VLSI circuits. Memristor is one of the promising candidates due to its scalability, practically zero leakage, non-volatility, etc. This paper proposes a novel design methodology for logic circuits targeting memristor crossbars. This methodology allows the optimization of the design of logic function, and their automatic mapping on the memristor crossbar. More important, this methodology supports the execution of Boolean logic functions within constant number of steps independent of its functionality. To illustrate the potential of the proposed methodology, multi-bit adders and multipliers are explored; their incurred delay, area and energy costs are analyzed. The comparison of our approach with state-of-the-art Boolean logic circuits for memristor crossbar architecture shows significant improvement in both delay (4 to 500 x) and energy consumption (1.22 to 3.71 x). The area overhead may decrease (down to 44%) or increase (up to 17%) depending on the circuit's functionality and logic optimization level.

/pdf/fast-boolean-logic-mapped-on-memristor-crossbar-20tqqpx30y.pdf

Fast boolean logic mapped on memristor crossbar

The industry is preparing itself for three-dimensional stacked ICs (3D-SICs), a technology that promises heterogeneous integration with higher performance and lower power dissipation at a smaller footprint. Several 3D stacking approaches are under development. From a yield point of view, Die-to-Wafer (D2W) stacking seems the most favorable approach, due to the ability of Known Good Die stacking. Minimizing the test cost for such a stacking approach is a challenging task. Every manufactured chip has to be tested, and any tiny test saving per 3D-SIC impacts the overall cost, especially in high-volume production. This paper establishes a cost model for D2W SICs and investigates the impact of the test cost for different test flows. It first introduces a framework covering different test flows for 3D D2W ICs. Subsequently, it proposes a test cost model to estimate the impact of the test flow on the overall 3D-SIC cost. Our simulation results show that (a) test flows with pre-bond testing significantly reduce the overall cost, (b) a cheaper test flow does not necessary result in lower overall cost, (c) test flows with intermediate tests (performed during the stacking process) pay off, (d) the most cost-effective test flow consists of pre-bond tests and strongly depends on the stack yield, hence, adapting the test according the stack yield is the best approach to use.

/pdf/test-cost-analysis-for-3d-die-to-wafer-stacking-3tx8atlocx.pdf

Mottaqiallah Taouil

Papers

Memristor based computation-in-memory architecture for data-intensive applications

Scouting Logic: A Novel Memristor-Based Logic Design for Resistive Computing

Testing Open Defects in Memristor-Based Memories

Fast boolean logic mapped on memristor crossbar

Test Cost Analysis for 3D Die-to-Wafer Stacking