Bibliographic record
Abstract
In most of the electronics and communication devices such as mobile, video phone and handheld video games low power and high density SRAM (Static Random Access Memory) is a favor. On the other hand, integration of many functions such as digital, memory, RF and analog circuits is necessary in near future. Scaling is one of the solutions to increase density of memories and functionality of integrated circuits. However, the increase of leakage current, process complexity and process variation of parameters limit scaling. This limitations force us to think about new dimension in integrated circuits. Three dimensional integrated circuits can solve some of the problems. They can give us low power, high density memories and high functionality circuits in same area of planar ICs. Different technologies can be merged in different layers of 3DIC to finally make a high performance system. In this thesis we realize SRAM cells in 3DIC to increase the capacity and performance of them. In chapter 2 we will introduce different memories and particular case SRAM. The principle of operation and design metrics are discussed in this chapter. Then in chapter 3 we talk about 3DIC and its advantages and disadvantages. Heat generation is main issue in 3DIC. New 3DIC fabrication technology called µ-Czochralski Process is introduced. Next in chapter 4 we design SRAM cells using analytic approach and we confirm the design by circuit simulation tools. Different SRAM cells and sense amplifier and output buffers are designed in this chapter. To fabricate design circuits we need layout for SRAM cells. In chapter 5 we extensively look to the design rules for SRAM circuits in one layer and two layers of silicon. Using double gate and H-Gate transistors to increase the performance of SRAM cells are discussed in this chapter. Then in chapter 6 we show fabrication process flow of one layer and two layers single grain silicon devices. Finally fabricated circuits are characterized electrically and results are reported in chapter 7.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".