emf (electro-magnetic field) simulation,lithography
Rigorous simulation of light through masks.
422 technical terms and definitions
Rigorous simulation of light through masks.
Systems to capture and neutralize hazardous emissions from tools.
Detect light from active devices.
Emissivity characterizes surface's effectiveness at emitting thermal radiation affecting radiative cooling.
Create custom emojis.
Detect emotions from text.
Detect emotions from text voice facial expressions.
Generate with emotional awareness.
Generate compassionate replies.
Generate responses that show understanding of emotions.
Maximize influence on future.
Empowerment is an intrinsic motivation measure quantifying an agent's ability to control its future states through mutual information maximization.
Efficient Neural Architecture Search reduces computational cost by sharing weights among child models through a single directed acyclic graph.
EnCodec combines convolutional encoder-decoder with residual VQ and adversarial training for audio compression.
Encoder-decoder models (T5) encode input then decode output. Good for translation, summarization.
Encoder-based inversion directly maps images to latent codes through learned networks.
Encoder-only models (BERT) see full context. Good for classification, embeddings. Not for generation.
Use encoder to find latent.
Full Transformer with both parts (T5 BART for seq2seq tasks).
Architecture with only encoder blocks (BERT for classification/understanding).
One-hot encode categorical features. Binary columns per category.
Robot blade or paddle that grips wafers.
Slowing scaling trends.
Wearout failures.
Defects at end of implant range.
Special token marking end.
End-to-end ASR directly maps audio to text without separate acoustic and language models.
Evaluate entire pipeline.
Learn entire SLAM pipeline.
# Semiconductor Manufacturing Etch Endpoint Process ## Overview In semiconductor fabrication, **etching** selectively removes material from wafers to create circuit patterns. The **endpoint detection problem** is determining precisely when to stop etching. $$ \text{Endpoint} = f(\text{target layer removal}, \text{underlayer preservation}) $$ ## The Core Challenge ### Why Endpoint Detection Matters - **Under-etching**: Leaves residual material → defects, shorts, incomplete patterns - **Over-etching**: Damages underlying layers → profile degradation, reliability issues At advanced nodes (3nm, 5nm), tolerances are measured in angstroms: $$ \Delta d_{\text{tolerance}} \approx 1-5 \text{ Å} $$ ## Primary Endpoint Detection Techniques ### 1. Optical Emission Spectroscopy (OES) The most widely used technique for plasma (dry) etching. #### Principle During plasma etching, reactive species and etch byproducts emit characteristic photons. The emission intensity $I(\lambda)$ at wavelength $\lambda$ follows: $$ I(\lambda) \propto n_{\text{species}} \cdot \sigma_{\text{emission}}(\lambda) \cdot E_{\text{plasma}} $$ Where: - $n_{\text{species}}$ = density of emitting species - $\sigma_{\text{emission}}$ = emission cross-section - $E_{\text{plasma}}$ = plasma excitation energy #### Key Wavelengths for Common Etch Chemistries | Species | Wavelength (nm) | Application | |---------|-----------------|-------------| | CO | 483.5, 519.8 | SiO₂ etch indicator | | F | 685.6, 703.7 | Fluorine radical monitoring | | Si | 288.2 | Silicon exposure detection | | Cl | 837.6 | Chlorine-based etch | | O | 777.4 | Oxygen monitoring | #### Signal Processing The endpoint is typically detected using derivative methods: $$ \frac{dI}{dt} = \lim_{\Delta t \to 0} \frac{I(t + \Delta t) - I(t)}{\Delta t} $$ Endpoint trigger condition: $$ \left| \frac{dI}{dt} \right| > \theta_{\text{threshold}} $$ #### Advantages - Non-contact, non-destructive measurement - Real-time monitoring capability - Works across entire wafer surface #### Limitations - Weak signals for very thin films ($d < 10$ nm) - Pattern density affects signal intensity - Requires optical access to plasma chamber ### 2. Laser Interferometry #### Principle A monochromatic laser beam reflects from the wafer surface. As etching progresses, film thickness changes alter the interference pattern. The reflected intensity follows: $$ I_{\text{reflected}} = I_1 + I_2 + 2\sqrt{I_1 I_2} \cos\left(\frac{4\pi n d}{\lambda} + \phi_0\right) $$ Where: - $I_1, I_2$ = intensities from top surface and interface reflections - $n$ = refractive index of the film - $d$ = film thickness - $\lambda$ = laser wavelength - $\phi_0$ = initial phase offset #### Fringe Analysis Each complete oscillation (fringe) corresponds to: $$ \Delta d_{\text{per fringe}} = \frac{\lambda}{2n} $$ **Example calculation** for SiO₂ with HeNe laser ($\lambda = 632.8$ nm): $$ \Delta d = \frac{632.8 \text{ nm}}{2 \times 1.46} \approx 216.7 \text{ nm/fringe} $$ #### Etch Rate Determination $$ \text{Etch Rate} = \frac{\lambda}{2n} \cdot \frac{1}{T_{\text{fringe}}} $$ Where $T_{\text{fringe}}$ is the period of one complete oscillation. #### Advantages - Quantitative thickness measurement - Real-time etch rate monitoring - High precision for transparent films #### Limitations - Requires optically transparent or semi-transparent films - Pattern density complicates signal interpretation - Multiple interfaces create complex interference ### 3. Residual Gas Analysis (Mass Spectrometry) #### Principle Analyze exhaust gas composition. Different materials produce different volatile byproducts: $$ \text{Material}_{\text{solid}} + \text{Etchant}_{\text{gas}} \rightarrow \text{Byproduct}_{\text{volatile}} $$ #### Example Reactions **Silicon etching with fluorine:** $$ \text{Si} + 4\text{F} \rightarrow \text{SiF}_4 \uparrow $$ **Oxide etching with fluorine:** $$ \text{SiO}_2 + 4\text{F} \rightarrow \text{SiF}_4 + \text{O}_2 \uparrow $$ **Aluminum etching with chlorine:** $$ \text{Al} + 3\text{Cl} \rightarrow \text{AlCl}_3 \uparrow $$ #### Mass-to-Charge Ratios | Byproduct | m/z | Parent Material | |-----------|-----|-----------------| | SiF₄ | 104 | Si, SiO₂ | | SiCl₄ | 170 | Si | | AlCl₃ | 133 | Al | | CO₂ | 44 | SiO₂, organics | | TiCl₄ | 190 | Ti, TiN | #### Advantages - Works regardless of optical properties - Chemically specific detection - Can detect multiple transitions #### Limitations - Response time limited by gas transport: $\tau \approx 0.5-2$ s - Requires differential pumping - Sensitivity issues at low etch rates ### 4. RF Impedance Monitoring #### Principle Plasma impedance changes when material composition changes. The plasma can be modeled as: $$ Z_{\text{plasma}} = R_{\text{plasma}} + j\omega L_{\text{plasma}} + \frac{1}{j\omega C_{\text{sheath}}} $$ #### Monitored Parameters - **Voltage**: $V_{\text{RF}}$ - **Current**: $I_{\text{RF}}$ - **Phase**: $\phi = \arctan\left(\frac{X}{R}\right)$ - **Impedance magnitude**: $|Z| = \sqrt{R^2 + X^2}$ #### Advantages - Uses existing RF infrastructure - No additional optical access needed - Sensitive to plasma chemistry changes #### Limitations - Subtle signal changes - Affected by many process parameters - Requires sophisticated signal processing ## Advanced Considerations ### Aspect Ratio Dependent Etching (ARDE) High aspect ratio (HAR) features etch slower due to transport limitations: $$ \text{Etch Rate}(AR) = \text{Etch Rate}_0 \cdot \exp\left(-\frac{AR}{AR_c}\right) $$ Where: - $AR = \frac{\text{depth}}{\text{width}}$ = aspect ratio - $AR_c$ = characteristic aspect ratio (process-dependent) **Consequence**: Dense arrays reach endpoint before isolated features. ### Pattern Loading Effect Local etch rate depends on pattern density $\rho$: $$ ER(\rho) = ER_{\text{open}} \cdot \frac{1}{1 + K \cdot \rho} $$ Where $K$ is the loading coefficient. ### Selectivity The selectivity $S$ between materials A and B: $$ S = \frac{ER_A}{ER_B} $$ **Higher selectivity allows more overetch margin:** $$ t_{\text{overetch,max}} = \frac{d_{\text{underlayer}} \cdot S}{ER_A} $$ ## Practical Endpoint Strategy ### Overetch Calculation Total etch time: $$ t_{\text{total}} = t_{\text{endpoint}} + t_{\text{overetch}} $$ Overetch percentage: $$ \text{Overetch \%} = \frac{t_{\text{overetch}}}{t_{\text{main}}} \times 100 $$ Typical values: 20-50% depending on uniformity and selectivity. ### Statistical Process Control Endpoint time follows a distribution: $$ t_{\text{EP}} \sim \mathcal{N}(\mu_{\text{EP}}, \sigma_{\text{EP}}^2) $$ Control limits: $$ \text{UCL} = \mu + 3\sigma, \quad \text{LCL} = \mu - 3\sigma $$ ## Multi-Sensor Fusion Modern systems combine multiple techniques: $$ \text{Endpoint}_{\text{final}} = \sum_{i} w_i \cdot \text{Signal}_i $$ Where weights $w_i$ are optimized by machine learning algorithms. ### Sensor Contributions | Sensor | Primary Detection | |--------|-------------------| | OES | Bulk composition change | | Interferometry | Precise thickness | | RF monitoring | Plasma state shifts | | Full-wafer imaging | Spatial uniformity | ## Key Equations Summary ### Interferometry $$ \boxed{\Delta d = \frac{\lambda}{2n}} $$ ### OES Endpoint Trigger $$ \boxed{\left| \frac{dI}{dt} \right| > \theta} $$ ### Selectivity $$ \boxed{S = \frac{ER_{\text{target}}}{ER_{\text{stop}}}} $$ ### ARDE Model $$ \boxed{ER(AR) = ER_0 \cdot e^{-AR/AR_c}} $$ ## Conclusion Etch endpoint detection is critical for: 1. **Yield**: Complete clearing without damage 2. **Uniformity**: Consistent results across wafer 3. **Reliability**: Device performance and longevity The combination of OES, interferometry, mass spectrometry, and RF monitoring—enhanced by machine learning—enables the precision required for sub-10nm semiconductor manufacturing.
Stop based on real-time detection.
Elemental analysis in SEM/TEM.
Energy efficiency in fabs focuses on reducing power consumption per wafer through equipment optimization process improvements and facility design.
Capture waste heat for reuse in other processes.
Energy-aware NAS optimizes architectures for minimal power consumption during inference.
Energy-based models assign scalar energy values to input-output configurations and make predictions by minimizing energy functions.
Models that assign energy to configurations.
Metric combining energy and speed.
Extended metric including area.
Generate interesting replies.
Control design modifications.
Document describing change.
Formal change to process or design.
Priority for development lots.
Small lots for development and testing.
Optimize designs for constraints.
Time used for development.
Improved decoding for masked positions.
Improve MD sampling efficiency.
Ensemble Kalman Filter uses Monte Carlo samples to represent state distributions in high-dimensional systems.