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sunnyvale

# Sunnyvale, California ## Overview **Sunnyvale** is a city located in **Santa Clara County, California**, positioned in the heart of **Silicon Valley**. The city derives its name from its characteristic sunny climate. ## Geographic & Demographic Data ### Location Coordinates $$ \text{Latitude: } 37.3688° \, N \quad \text{Longitude: } 122.0363° \, W $$ ### Population Statistics - **Estimated Population**: $\approx 155,000 - 160,000$ residents - **Population Density**: $\approx 6,500 \, \text{people/mi}^2$ - **Land Area**: $\approx 22.0 \, \text{mi}^2$ or $57.0 \, \text{km}^2$ $$ \text{Population Density} = \frac{\text{Population}}{\text{Area}} = \frac{155,000}{22.0} \approx 7,045 \, \text{people/mi}^2 $$ ### Elevation $$ \text{Elevation} \approx 40 \, \text{ft} \approx 12 \, \text{m} \quad \text{(above sea level)} $$ ## Climate Data Sunnyvale features a **Mediterranean climate** (Köppen classification: $Csb$). ### Average Temperature | Season | Temperature (°F) | Temperature (°C) | |--------|------------------|------------------| | Summer High | $82°F$ | $\frac{5}{9}(82-32) \approx 28°C$ | | Winter Low | $42°F$ | $\frac{5}{9}(42-32) \approx 6°C$ | $$ T_{°C} = \frac{5}{9}(T_{°F} - 32) $$ ### Annual Rainfall $$ \text{Average Annual Precipitation} \approx 15.5 \, \text{inches} \approx 394 \, \text{mm} $$ ### Sunny Days $$ \text{Average Sunny Days per Year} \approx 257 \, \text{days} $$ $$ \text{Sunshine Percentage} = \frac{257}{365} \times 100 \approx 70.4\% $$ ## Economic Profile ### Major Industries - **Technology** - Software development - Hardware manufacturing - Cloud computing - Artificial intelligence - **Aerospace & Defense** - Lockheed Martin Space - Historical defense contractors - **Semiconductors** - AMD (Advanced Micro Devices) - Various chip design firms ### Notable Companies Headquartered in Sunnyvale - **LinkedIn** (Microsoft subsidiary) - **Yahoo** (historical HQ) - **AMD** (Advanced Micro Devices) - **Juniper Networks** - **NetApp** - **Fortinet** - **Trimble Inc.** ### Economic Formulas #### Median Household Income $$ \text{Median Household Income} \approx 140{,}000 - 160{,}000 \, \text{USD} $$ #### Cost of Living Index $$ \text{Cost of Living Index} \approx 180 - 200 \quad (\text{US Average} = 100) $$ $$ \text{Relative Cost} = \frac{\text{Sunnyvale COL}}{\text{US Average COL}} = \frac{190}{100} = 1.9x $$ ## Real Estate Metrics ### Housing Prices $$ \text{Median Home Price} \approx 1{,}800{,}000 - 2{,}200{,}000 \, \text{USD} $$ ### Price Per Square Foot $$ \text{Price per ft}^2 \approx 1{,}000 - 1{,}400 \, \text{USD} $$ ### Mortgage Calculation Example For a $2{,}000{,}000 USD home with $20\%$ down payment at $7\%$ interest over $30$ years: $$ \text{Principal} = 2{,}000{,}000 \times 0.80 = 1{,}600{,}000 \, \text{USD} $$ Monthly payment formula: $$ M = P \times \frac{r(1+r)^n}{(1+r)^n - 1} $$ Where: - $P = 1{,}600{,}000 \, \text{USD}$ (principal) - $r = \frac{0.07}{12} \approx 0.00583$ (monthly interest rate) - $n = 360$ (total months) $$ M = 1{,}600{,}000 \times \frac{0.00583(1.00583)^{360}}{(1.00583)^{360} - 1} \approx 10{,}644 \, \text{USD/month} $$ ## Transportation ### Distance to Major Cities | Destination | Distance (miles) | Distance (km) | |-------------|------------------|---------------| | San Jose | $\approx 8$ | $\approx 13$ | | San Francisco | $\approx 40$ | $\approx 64$ | | Oakland | $\approx 35$ | $\approx 56$ | $$ \text{Distance}_{km} = \text{Distance}_{mi} \times 1.60934 $$ ### Commute Statistics - **Average Commute Time**: $\approx 28 - 32 \, \text{minutes}$ - **Public Transit Options**: - Caltrain (commuter rail) - VTA (Valley Transportation Authority) buses and light rail - ACE Train access nearby $$ \text{Annual Commute Hours} = \text{Daily Commute} \times \text{Work Days} = 1 \, \text{hr} \times 250 = 250 \, \text{hours/year} $$ ## Historical Timeline ### Key Dates - **1850s**: Agricultural settlement (orchards) - **1901**: Incorporated as a city - **1912**: Population $\approx 1,700$ - **1956**: Lockheed Missiles & Space Division established - **1990s-2000s**: Tech boom transformation - **Present**: Major Silicon Valley hub ### Population Growth Model Exponential growth approximation: $$ P(t) = P_0 \times e^{rt} $$ Where: - $P_0$ = initial population - $r$ = growth rate - $t$ = time in years ## Education ### School District Statistics - **Sunnyvale School District** (K-8) - **Fremont Union High School District** (9-12) #### Student-to-Teacher Ratio $$ \text{Ratio} = \frac{\text{Students}}{\text{Teachers}} \approx \frac{22}{1} = 22:1 $$ ### Nearby Universities - **Stanford University**: $\approx 10 \, \text{miles}$ - **Santa Clara University**: $\approx 5 \, \text{miles}$ - **San Jose State University**: $\approx 10 \, \text{miles}$ ## Points of Interest ### Parks & Recreation - **Baylands Park** - Area: $\approx 50 \, \text{acres}$ - Features: wetlands, trails, wildlife - **Las Palmas Park** - Tennis courts - Playground facilities - **Columbia Park** - Swimming pool - Sports fields ### Downtown Murphy Avenue - **Length**: $\approx 0.5 \, \text{miles}$ of shops/restaurants - **Establishments**: $\approx 50+$ businesses ## Technology Metrics ### Tech Employment $$ \text{Tech Workforce} \approx 35\% - 40\% \text{ of total employment} $$ ### Patent Activity $$ \text{Patents Filed Annually (Silicon Valley)} \approx 15,000 - 20,000 $$ ### Startup Density $$ \text{Startups per } 10,000 \text{ residents} \approx 15 - 25 $$ ## Summary Statistics | Metric | Value | |--------|-------| | Population | $\sim 155,000$ | | Area | $22.0 \, \text{mi}^2$ | | Elevation | $40 \, \text{ft}$ | | Median Income | $\sim 150{,}000 \, \text{USD}$ | | Median Home Price | $\sim 2{,}000{,}000 \, \text{USD}$ | | Sunny Days | $257 \, \text{days/year}$ | | Major Industry | Technology | ## Mathematical ### Useful Conversions $$ \begin{aligned} 1 \, \text{mile} &= 1.60934 \, \text{km} \\ 1 \, \text{ft}^2 &= 0.0929 \, \text{m}^2 \\ 1 \, \text{acre} &= 43,560 \, \text{ft}^2 = 4,047 \, \text{m}^2 \\ °C &= \frac{5}{9}(°F - 32) \end{aligned} $$ ### Statistical Formulas Used #### Mean $$ \bar{x} = \frac{1}{n} \sum_{i=1}^{n} x_i $$ #### Standard Deviation $$ \sigma = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (x_i - \bar{x})^2} $$ #### Compound Growth Rate $$ \text{CAGR} = \left( \frac{V_f}{V_i} \right)^{\frac{1}{t}} - 1 $$

supabase,postgres,open

Supabase is open source Firebase alternative. PostgreSQL.

super hot lot, operations

Highest priority lot.

super steep retrograde (ssr) well,process

Very abrupt doping profile.

super-naturalinstructions, data

Large instruction dataset.

super-resolution ai,computer vision

Upscale images to higher resolution using deep learning.

super-steep retrograde, process integration

Super-steep retrograde profiles combine abrupt junctions with retrograde wells for advanced scaling.

superconducting transition temperature prediction, materials science

Predict Tc for superconductors.

supercritical co2 drying, process

Use supercritical fluid.

superglue, evaluation

Suite of difficult language understanding tasks.

superglue, evaluation

SuperGLUE includes harder language understanding tasks than original GLUE.

superglue,evaluation

More challenging version of GLUE.

supermarket, manufacturing operations

Supermarkets are controlled inventory locations supplying downstream processes on pull signals.

supermasks,model optimization

Binary masks that work without training.

supernet training, neural architecture

Train network containing all candidate architectures.

supernet training, neural architecture search

Supernet training creates a weight-sharing over-parameterized network encompassing all candidate architectures for efficient performance estimation.

superpod, infrastructure

Large-scale DGX cluster.

superposition hypothesis, explainable ai

Networks pack more features than dimensions.

superposition,feature,polysemantic

Superposition: models encode more features than neurons. Polysemantic neurons respond to multiple concepts.

supervised contrastive learning, self-supervised learning

Use labels to improve contrastive learning.

supervised,sft,finetune data

Supervised Fine-Tuning (SFT) uses input to output pairs (chat transcripts, QA, code fixes) to teach a model specific behaviors before RLHF/DPO.

supervisely,computer vision,label

Supervisely focuses on computer vision annotation. Segmentation, video.

supplier audit, supply chain & logistics

Supplier audits systematically evaluate vendor facilities processes and quality systems ensuring compliance with requirements.

supplier consolidation, supply chain & logistics

Supplier consolidation reduces vendor count leveraging volume for better terms and simplified management.

supplier development, supply chain & logistics

Supplier development programs improve vendor capabilities through training audits and collaborative improvement.

supplier performance management, quality

Track supplier reliability.

supplier performance, supply chain & logistics

Supplier performance metrics track quality delivery reliability and responsiveness for vendor management.

supplier qualification,quality

Approve material sources.

supplier scorecard, supply chain & logistics

Supplier scorecards track vendor performance across metrics like quality delivery and cost providing feedback for continuous improvement.

supply chain for chiplets, business

Ecosystem for chiplet-based design.

supply chain integration, supply chain & logistics

Supply chain integration connects information systems across partners enabling seamless data flow.

supply chain logistics,operations

Manage material flow.

supply chain risk, supply chain & logistics

Supply chain risk in semiconductor manufacturing includes material shortages supplier failures geopolitical disruptions and lead time variability.

supply chain visibility, supply chain & logistics

Supply chain visibility provides real-time tracking of materials and components throughout semiconductor manufacturing.

supply chain,dependency,security

ML supply chain risks: malicious models, poisoned datasets, vulnerable dependencies. Verify sources.

supply chain,industry

Network of suppliers providing materials equipment and services.

supply line, manufacturing equipment

Supply lines deliver chemicals from storage to process equipment.

support set,few-shot learning

Training examples available during few-shot evaluation.

support vector machines for classification, svm, data analysis

SVM for classifying wafers or dies.

surface code, quantum ai

Leading quantum error correction code.

surface damage from grinding, process

Subsurface defects from mechanical grinding.

surface energy measurement, metrology

Characterize surface properties.

surface micromachining, process

Build structures on surface.

surface mount technology, smt, packaging

Mount on PCB surface.

surface passivation,process

Reduce surface recombination with interface treatment.

surface photovoltage spectroscopy, sps, metrology

Measure band bending and carrier diffusion.

surface photovoltage, spv, metrology

Measure minority carrier diffusion length.

surface preparation for bonding, advanced packaging

Clean and activate surfaces before bonding.

surface recombination velocity, device physics

Recombination rate at surface.

surface recombination, device physics

Recombination at surfaces.