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    ๋ฌผ๋ฆฌ์  ์„ค๊ณ„ ์ž๋™ํ™”์—์„œ ํ‘œ์ค€์…€ ํ•ฉ์„ฑ ๋ฐ ์ตœ์ ํ™”์™€ ์„ค๊ณ„ ํ’ˆ์งˆ ์˜ˆ์ธก ๋ฐฉ๋ฒ•๋ก 

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2023. 2. ๊น€ํƒœํ™˜.In the physical design of chip implementation, designing high-quality standard cell layout and accurately predicting post-route DRV (design rule violation) at an early stage is an important problem, especially in advanced technology nodes. This dissertation presents two methodologies that can contribute to improving the design quality and design turnaround time of physical design flow. Firstly, we propose an integrated approach to the two problems of transistor folding and placement in standard cell layout synthesis. Precisely, we propose a globally optimal algorithm of search tree based design space exploration, devising a set of effective speeding up techniques as well as dynamic programming based fast cost computation. In addition, our algorithm incorporates the minimum oxide diffusion jog constraint, which closely relies on both of transistor folding and placement. Through experiments with the transistor netlists and design rules in advanced node, our proposed method is able to synthesize fully routable cell layouts of minimal size within a very fast time for each netlist, outperforming the cell layout quality in the manual design. Secondly, we propose a novel ML based DRC hotspot prediction technique, which is able to accurately capture the combined impact of pin accessibility and routing congestion on DRC hotspots. Precisely, we devise a graph, called pin proximity graph, that effectively models the spatial information on cell I/O pins and the information on pin-to-pin disturbance relation. Then, we propose a new ML model, which tightly combines GNN (graph neural network) and U-net in a way that GNN is used to embed pin accessibility information abstracted from our pin proximity graph while U-net is used to extract routing congestion information from grid-based features. Through experiments with a set of benchmark designs using advanced node, our model outperforms the existing ML models on all benchmark designs within the fast inference time in comparison with that of the state-of-the-art techniques.์นฉ ๊ตฌํ˜„์˜ ๋ฌผ๋ฆฌ์  ์„ค๊ณ„ ๋‹จ๊ณ„์—์„œ, ๋†’์€ ์„ฑ๋Šฅ์˜ ํ‘œ์ค€ ์…€ ์„ค๊ณ„์™€ ๋ฐฐ์„  ์—ฐ๊ฒฐ ์ดํ›„ ์กฐ๊ธฐ์— ์„ค๊ณ„ ๊ทœ์น™ ์œ„๋ฐ˜์„ ์ •ํ™•ํžˆ ์˜ˆ์ธกํ•˜๋Š” ๊ฒƒ์€ ์ตœ์‹  ๊ณต์ •์—์„œ ํŠนํžˆ ์ค‘์š”ํ•œ ๋ฌธ์ œ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ฌผ๋ฆฌ์  ์„ค๊ณ„์—์„œ์˜ ์„ค๊ณ„ ํ’ˆ์งˆ๊ณผ ์ด ์„ค๊ณ„ ์‹œ๊ฐ„ ํ–ฅ์ƒ์„ ๋‹ฌ์„ฑํ•  ์ˆ˜ ์žˆ๋Š” ๋‘ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์•ˆํ•œ๋‹ค. ๋จผ์ €, ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ‘œ์ค€ ์…€ ๋ ˆ์ด์•„์›ƒ ํ•ฉ์„ฑ์—์„œ ํŠธ๋žœ์ง€์Šคํ„ฐ ํด๋”ฉ๊ณผ ๋ฐฐ์น˜๋ฅผ ์ข…ํ•ฉ์ ์œผ๋กœ ์ง„ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ๋…ผํ•œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ ํƒ์ƒ‰ ํŠธ๋ฆฌ ๊ธฐ๋ฐ˜์˜ ์ตœ์ ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๋™์  ํ”„๋กœ๊ทธ๋ž˜๋ฐ ๊ธฐ๋ฐ˜ ๋น ๋ฅธ ๋น„์šฉ ๊ณ„์‚ฐ ๋ฐฉ๋ฒ•๊ณผ ์—ฌ๋Ÿฌ ์†๋„ ๊ฐœ์„  ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์—ฌ๊ธฐ์— ๋”ํ•ด, ์ตœ์‹  ๊ณต์ •์—์„œ ํŠธ๋žœ์ง€์Šคํ„ฐ ํด๋”ฉ๊ณผ ๋ฐฐ์น˜๋กœ ์ธํ•ด ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ์ตœ์†Œ ์‚ฐํ™”๋ฌผ ํ™•์‚ฐ ์˜์—ญ ์„ค๊ณ„ ๊ทœ์น™์„ ๊ณ ๋ คํ•˜์˜€๋‹ค. ์ตœ์‹  ๊ณต์ •์— ๋Œ€ํ•œ ํ‘œ์ค€ ์…€ ํ•ฉ์„ฑ ์‹คํ—˜ ๊ฒฐ๊ณผ, ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์ด ์„ค๊ณ„ ์ „๋ฌธ๊ฐ€๊ฐ€ ์ˆ˜๋™์œผ๋กœ ์„ค๊ณ„ํ•œ ๊ฒƒ ๋Œ€๋น„ ๋†’์€ ์„ฑ๋Šฅ์„ ๋ณด์ด๊ณ , ์„ค๊ณ„ ์‹œ๊ฐ„๋„ ๋งค์šฐ ์งง์Œ์„ ๋ณด์ธ๋‹ค. ๋‘๋ฒˆ์งธ๋กœ, ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์…€ ๋ฐฐ์น˜ ๋‹จ๊ณ„์—์„œ ํ•€ ์ ‘๊ทผ์„ฑ๊ณผ ์—ฐ๊ฒฐ ํ˜ผ์žก์œผ๋กœ ์ธํ•œ ์˜ํ–ฅ์„ ์ข…ํ•ฉ์ ์œผ๋กœ ๊ณ ๋ คํ•  ์ˆ˜ ์žˆ๋Š” ๋จธ์‹  ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ ์„ค๊ณ„ ๊ทœ์น™ ์œ„๋ฐ˜ ๊ตฌ์—ญ ์˜ˆ์ธก ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์•ˆํ•œ๋‹ค. ๋จผ์ € ํ‘œ์ค€ ์…€์˜ ์ž…/์ถœ๋ ฅ ํ•€์˜ ๋ฌผ๋ฆฌ์  ์ •๋ณด์™€ ํ•€๊ณผ ํ•€ ์‚ฌ์ด ๋ฐฉํ•ด ๊ด€๊ณ„๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋Š” ํ•€ ๊ทผ์ ‘ ๊ทธ๋ž˜ํ”„๋ฅผ ์ œ์•ˆํ•˜๊ณ , ๊ทธ๋ž˜ํ”„ ์‹ ๊ฒฝ๋ง๊ณผ ์œ ๋„ท ์‹ ๊ฒฝ๋ง์„ ํšจ๊ณผ์ ์œผ๋กœ ๊ฒฐํ•ฉํ•œ ์ƒˆ๋กœ์šด ํ˜•ํƒœ์˜ ๋จธ์‹  ๋Ÿฌ๋‹ ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ๋ชจ๋ธ์—์„œ ๊ทธ๋ž˜ํ”„ ์‹ ๊ฒฝ๋ง์€ ํ•€ ๊ทผ์ ‘ ๊ทธ๋ž˜ํ”„๋กœ๋ถ€ํ„ฐ ํ•€ ์ ‘๊ทผ์„ฑ ์ •๋ณด๋ฅผ ์ถ”์ถœํ•˜๊ณ , ์œ ๋„ท ์‹ ๊ฒฝ๋ง์€ ๊ฒฉ์ž ๊ธฐ๋ฐ˜ ํŠน์ง•์œผ๋กœ๋ถ€ํ„ฐ ์—ฐ๊ฒฐ ํ˜ผ์žก ์ •๋ณด๋ฅผ ์ถ”์ถœํ•œ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์€ ์ด์ „ ์—ฐ๊ตฌ๋“ค ๋Œ€๋น„ ๋” ๋น ๋ฅธ ์˜ˆ์ธก ์‹œ๊ฐ„์— ๋” ๋†’์€ ์˜ˆ์ธก ์„ฑ๋Šฅ์„ ๋‹ฌ์„ฑํ•จ์„ ๋ณด์ธ๋‹ค.1 Introduction 1 1.1 Standard Cell Layout Synthesis 1 1.2 Machine Learning for Electronic Design Automation 6 1.3 Prediction of Design Rule Violation 8 1.4 Contributions of This Dissertation 11 2 Standard Cell Layout Synthesis of Advanced Nodes with Simultaneous Transistor Folding and Placement 14 2.1 Motivations 14 2.2 Algorithm for Standard Cell Layout Synthesis 16 2.2.1 Problem Definition 16 2.2.2 Overall Flow 18 2.2.3 Step 1: Generation of Folding Shapes 18 2.2.4 Step 2: Search-tree Based Design Space Exploration 20 2.2.5 Speeding up Techniques 23 2.2.6 In-cell Routability Estimation 28 2.2.7 Step 3: In-cell Routing 30 2.2.8 Step 4: Splitting Folding Shapes 35 2.2.9 Step 5: Relaxing Minimum-area Constraints 37 2.3 Experimental Results 38 2.3.1 Comparison with ASAP 7nm Cell Layouts 40 2.3.2 Effectiveness of Dynamic Folding 42 2.3.3 Effectiveness of Speeding Up Techniques 43 2.3.4 Impact of Splitting Folding Shape 48 2.3.5 Runtime Analysis According to Area Relaxation 51 2.3.6 Comparison with Previous Works 52 3 Pin Accessibility and Routing Congestion Aware DRC Hotspot Prediction using Graph Neural Network and U-Net 54 3.1 Preliminary 54 3.1.1 Graph Neural Network 54 3.1.2 Fully Convolutional Network 56 3.2 Proposed Prediction Methodology 57 3.2.1 Overall Flow 57 3.2.2 Pin Proximity Graph 58 3.2.3 Grid-based Features 61 3.2.4 Overall Architecture of PGNN 64 3.2.5 GNN Architecture in PGNN 64 3.2.6 U-net Architecture in PGNN 66 3.2.7 Final Prediction in PGNN 66 3.3 Experimental Results 68 3.3.1 Experimental Setup 68 3.3.2 Analysis on PGNN Performance 71 3.3.3 Comparison with Previous Works 72 3.3.4 Adaptation to Real-world Designs 81 3.3.5 Handling Data Imbalance Problem in Regression Model 86 4 Conclusions 92 4.1 Chapter 2 92 4.2 Chapter 3 93๋ฐ•

    A basic building block approach to CMOS design of analog neuro/fuzzy systems

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    Outlines a systematic approach to design fuzzy inference systems using analog integrated circuits in standard CMOS VLSI technologies. The proposed circuit building blocks are arranged in a layered neuro/fuzzy architecture composed of 5 layers: fuzzification, T-norm, normalization, consequent, and output. Inference is performed by using Takagi and Sugeno's (1989) IF-THEN rules, particularly where the rule's output contains only a constant term-a singleton. A simple CMOS circuit with tunable bell-like transfer characteristics is used for the fuzzification. The inputs to this circuit are voltages while the outputs are currents. Circuit blocks proposed for the remaining layers operate in the current-mode domain. Innovative circuits are proposed for the T-norm and normalization layers. The other two layers use current mirrors and KCL. All the proposed circuits emphasize simplicity at the circuit level-a prerequisite to increasing system level complexity and operation speed. A 3-input, 4-rule controller has been designed for demonstration purposes in a 1.6 /spl mu/m CMOS single-poly, double-metal technology. We include measurements from prototypes of the membership function block and detailed HSPICE simulations of the whole controller. These results operation speed in the range of 5 MFLIPS (million fuzzy logic inferences per second) with systematic errors below 1%

    The predictor-adaptor paradigm : automation of custom layout by flexible design

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    Standard Transistor Array (STAR). Volume 1: Placement technique

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    A large scale integration (LSI) technology, the standard transistor array uses a prefabricated understructure of transistors and a comprehensive library of digital logic cells to allow efficient fabrication of semicustom digital LSI circuits. The cell placement technique for this technology involves formation of a one dimensional cell layout and "folding" of the one dimensional placement onto the chip. It was found that, by use of various folding methods, high quality chip layouts can be achieved. Methods developed to measure of the "goodness" of the generated placements include efficient means for estimating channel usage requirements and for via counting. The placement and rating techniques were incorporated into a placement program (CAPSTAR). By means of repetitive use of the folding methods and simple placement improvement strategies, this program provides near optimum placements in a reasonable amount of time. The program was tested on several typical LSI circuits to provide performance comparisons both with respect to input parameters and with respect to the performance of other placement techniques. The results of this testing indicate that near optimum placements can be achieved by use of the procedures incurring severe time penalties
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