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 －

A geometric program (GP) is a type of mathematical optimization problem characterized by objective and constraint functions that have a special form. Recently developed solution methods can solve even large-scale GPs extremely efficiently and reliably; at the same time a number of practical problems, particularly in circuit design, have been found to be equivalent to (or well approximated by) GPs. Putting these two together, we get effective solutions for the practical problems. The basic approach in GP modeling is to attempt to express a practical problem, such as an engineering analysis or design problem, in GP format. In the best case, this formulation is exact; when this is not possible, we settle for an approximate formulation. This tutorial paper collects together in one place the basic background material needed to do GP modeling. We start with the basic definitions and facts, and some methods used to transform problems into GP format. We show how to recognize functions and problems compatible with GP, and how to approximate functions or data in a form compatible with GP (when this is possible). We give some simple and representative examples, and also describe some common extensions of GP, along with methods for solving (or approximately solving) them.

/pdf/a-tutorial-on-geometric-programming-47sdznbzxw.pdf

A tutorial on geometric programming

我的台灣, 看見心靈的故鄉 =2009林磐聳藝術與設計展

IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society

The main characteristic of Reconfigurable Computing is the presence of hardware that can be reconfigured to implement specific functionality more suitable for specially tailored hardware than on a simple uniprocessor. Reconfigurable computing systems join microprocessors and programmable hardware in order to take advantage of the combined strengths of hardware and software and have been used in applications ranging from embedded systems to high performance computing. Many of the fundamental theories have been identified and used by the Hardware/Software Co-Design research field. Although the same background ideas are shared in both areas, they have different goals and use different approaches.This book is intended as an introduction to the entire range of issues important to reconfigurable computing, using FPGAs as the context, or "computing vehicles" to implement this powerful technology. It will take a reader with a background in the basics of digital design and software programming and provide them with the knowledge needed to be an effective designer or researcher in this rapidly evolving field.

· Treatment of FPGAs as computing vehicles rather than glue-logic or ASIC substitutes
· Views of FPGA programming beyond Verilog/VHDL
· Broad set of case studies demonstrating how to use FPGAs in novel and efficient ways

Reconfigurable Computing: The Theory and Practice of FPGA-Based Computation

Because of the widening sub-wavelength lithography gap in advanced fabrication technology, lithography hotspot detection has become an essential task in design for manufacturability. Unlike current state-of-the-art works, which unite pattern matching and machine-learning engines, we fully exploit the strengths of machine learning using novel techniques. By combing topological classification and critical feature extraction, our hotspot detection framework achieves very high accuracy. Furthermore, to speed-up the evaluation, we verify only possible layout clips instead of full-layout scanning. We utilize feedback learning and present redundant clip removal to reduce the false alarm. Experimental results show that the proposed framework is very accurate and demonstrates a rapid training convergence. Moreover, our framework outperforms the 2012 CAD contest at International Conference on Computer-Aided Design (ICCAD) winner on accuracy and false alarm.

Machine-Learning-Based Hotspot Detection Using Topological Classification and Critical Feature Extraction

In advanced fabrication technology, the sub-wavelength lithography gap causes unwanted layout distortions. Even if a layout passes design rule checking (DRC), it still might contain process hotspots, which are sensitive to the lithographic process. Hence, process-hotspot detection has become a crucial issue. In this paper, we propose an accurate process-hotspot detection framework. Unlike existing DRC-based works, we extract only critical design rules to express the topological features of hotspot patterns. We adopt a two-stage filtering process to locate all hotspots accurately and efficiently. Compared with state-of-the-art DRC-based works, our results show that our approach can reach 100% success rate with significant speedups.

Accurate process-hotspot detection using critical design rule extraction

Because of the widening sub-wavelength lithography gap in advanced fabrication technology, lithography hotspot detection has become an essential task in design for manufacturability. Current state-of-the-art works unite pattern matching and machine learning engines. Unlike them, we fully exploit the strengths of machine learning using novel techniques. By combing topological classification and critical feature extraction, our hotspot detection framework achieves very high accuracy. Furthermore, to speed up the evaluation, we verify only possible layout clips instead of full-layout scanning. After detection, we filter hotspots to reduce the false alarm. Experimental results show that the proposed framework is very accurate and demonstrates a rapid training convergence. Moreover, our framework outperforms the 2012 CAD Contest at ICCAD winner on accuracy and false alarm.

Machine-learning-based hotspot detection using topological classification and critical feature extraction

Noise, as well as area, delay, and power, is one of the most important concerns in the design of deep submicrometer integrated circuits. Currently existing algorithms do not handle simultaneous switching conditions of signals for noise minimization. In this paper, we model not only physical coupling capacitance, but also simultaneous switching behavior for noise optimization. Based on Lagrangian relaxation, we present an algorithm which can optimally solve the simultaneous noise, area, delay, and power optimization problem by sizing circuit components. Our algorithm, with linear memory requirement and linear runtime, is very effective and efficient. For example, for a circuit of 6144 wires and 3512 gates, our algorithm solves the simultaneous optimization problem using only 2.1-MB memory and 19.4-min runtime to achieve the precision of within 1% error on a SUN Spare Ultra-I workstation.

/pdf/crosstalk-driven-interconnect-optimization-by-simultaneous-497gts724u.pdf

Crosstalk-driven interconnect optimization by simultaneous gate and wire sizing

Clock power is the major contributor to dynamic power for modern integrated circuit design. A conventional single-bit flip-flop cell uses an inverter chain with a high drive strength to drive the clock signal. Clustering several such cells and forming a multibit flip-flop can share the drive strength, dynamic power, and area of the inverter chain, and can even save the clock network power and facilitate the skew control. Hence, in this paper, we focus on postplacement multibit flip-flop clustering to gain these benefits. Utilizing the properties of Manhattan distance and coordinate transformation, we model the problem instance by two interval graphs and use a pair of linear-sized sequences as our representation. Without enumerating all possible combinations, we identify only partial sequences that are necessary to cluster flip-flops, thus leading to an efficient clustering scheme. Moreover, our fast coordinate transformation also makes the execution of our algorithm very efficient. The experiments are conducted on industrial circuits. Our results show that concise representation delivers superior efficiency and effectiveness. Even under timing and placement density constraints, clock power saving via multibit flip-flop clustering can still be substantial at postplacement.

https://ir.nctu.edu.tw:443/bitstream/11536/15241/1/000299507800004.pdf

Iris Hui-Ru Jiang

Papers

Machine-Learning-Based Hotspot Detection Using Topological Classification and Critical Feature Extraction

Accurate process-hotspot detection using critical design rule extraction

Machine-learning-based hotspot detection using topological classification and critical feature extraction

Crosstalk-driven interconnect optimization by simultaneous gate and wire sizing

INTEGRA: Fast Multibit Flip-Flop Clustering for Clock Power Saving