point-e, multimodal ai
Point-E generates 3D point clouds from text using diffusion models.
758 technical terms and definitions
Point-E generates 3D point clouds from text using diffusion models.
Filter installed close to tool to ensure ultra-pure chemicals or gases.
Point-of-use abatement treats emissions at each process tool rather than centrally.
Point-of-use filters remove particles immediately before chemical delivery.
1x1 convolution for channel mixing.
Pointwise convolutions use 1x1 kernels for channel mixing without spatial interaction.
Pointwise ranking predicts absolute relevance scores for items independently.
Score each item independently.
Corrupt training data to degrade performance.
Relates potential to charge distribution.
# Semiconductor Manufacturing Process: Poisson Statistics & Mathematical Modeling ## 1. Introduction: Why Poisson Statistics? Semiconductor defects satisfy the classical **Poisson conditions**: - **Rare events** — Defects are sparse relative to the total chip area - **Independence** — Defect occurrences are approximately independent - **Homogeneity** — Within local regions, defect rates are constant - **No simultaneity** — At infinitesimal scales, simultaneous defects have zero probability ### 1.1 The Poisson Probability Mass Function The probability of observing exactly $k$ defects: $$ P(X = k) = \frac{\lambda^k e^{-\lambda}}{k!} $$ where the expected number of defects is: $$ \lambda = D_0 \cdot A $$ **Parameter definitions:** - $D_0$ — Defect density (defects per unit area, typically defects/cm²) - $A$ — Chip area (cm²) - $\lambda$ — Mean number of defects per chip ### 1.2 Key Statistical Properties | Property | Formula | |----------|---------| | Mean | $E[X] = \lambda$ | | Variance | $\text{Var}(X) = \lambda$ | | Variance-to-Mean Ratio | $\frac{\text{Var}(X)}{E[X]} = 1$ | > **Note:** The equality of mean and variance (equidispersion) is a signature property of the Poisson distribution. Real semiconductor data often shows **overdispersion** (variance > mean), motivating compound models. ## 2. Fundamental Yield Equation ### 2.1 The Seeds Model (Simple Poisson) A chip is functional if and only if it has **zero killer defects**. Under Poisson assumptions: $$ \boxed{Y = P(X = 0) = e^{-D_0 A}} $$ **Derivation:** $$ P(X = 0) = \frac{\lambda^0 e^{-\lambda}}{0!} = e^{-\lambda} = e^{-D_0 A} $$ ### 2.2 Limitations of Simple Poisson - Assumes **uniform** defect density across the wafer (unrealistic) - Does not account for **clustering** of defects - Consistently **underestimates** yield for large chips - Ignores wafer-to-wafer and lot-to-lot variation ## 3. Compound Poisson Models ### 3.1 The Negative Binomial Approach Model the defect density $D_0$ as a **random variable** with Gamma distribution: $$ D_0 \sim \text{Gamma}\left(\alpha, \frac{\alpha}{\bar{D}}\right) $$ **Gamma probability density function:** $$ f(D_0) = \frac{(\alpha/\bar{D})^\alpha}{\Gamma(\alpha)} D_0^{\alpha-1} e^{-\alpha D_0/\bar{D}} $$ where: - $\bar{D}$ — Mean defect density - $\alpha$ — Clustering parameter (shape parameter) ### 3.2 Resulting Yield Model When defect density is Gamma-distributed, the defect count follows a **Negative Binomial** distribution, yielding: $$ \boxed{Y = \left(1 + \frac{D_0 A}{\alpha}\right)^{-\alpha}} $$ ### 3.3 Physical Interpretation of Clustering Parameter $\alpha$ | $\alpha$ Value | Physical Interpretation | |----------------|------------------------| | $\alpha \to \infty$ | Uniform defects — recovers simple Poisson model | | $\alpha \approx 1-5$ | Typical semiconductor clustering | | $\alpha \to 0$ | Extreme clustering — defects occur in tight groups | ### 3.4 Overdispersion The variance-to-mean ratio for the Negative Binomial: $$ \frac{\text{Var}(X)}{E[X]} = 1 + \frac{\bar{D}A}{\alpha} > 1 $$ This **overdispersion** (ratio > 1) matches empirical observations in semiconductor manufacturing. ## 4. Classical Yield Models ### 4.1 Comparison Table | Model | Yield Formula | Assumed Density Distribution | |-------|---------------|------------------------------| | Seeds (Poisson) | $Y = e^{-D_0 A}$ | Delta function (uniform) | | Murphy | $Y = \left(\frac{1 - e^{-D_0 A}}{D_0 A}\right)^2$ | Triangular | | Negative Binomial | $Y = \left(1 + \frac{D_0 A}{\alpha}\right)^{-\alpha}$ | Gamma | | Moore | $Y = e^{-\sqrt{D_0 A}}$ | Empirical | | Bose-Einstein | $Y = \frac{1}{1 + D_0 A}$ | Exponential | ### 4.2 Murphy's Yield Model Assumes triangular distribution of defect densities: $$ Y_{\text{Murphy}} = \left(\frac{1 - e^{-D_0 A}}{D_0 A}\right)^2 $$ **Taylor expansion for small $D_0 A$:** $$ Y_{\text{Murphy}} \approx 1 - \frac{(D_0 A)^2}{12} + O((D_0 A)^4) $$ ### 4.3 Limiting Behavior As $D_0 A \to 0$ (low defect density): $$ \lim_{D_0 A \to 0} Y = 1 \quad \text{(all models)} $$ As $D_0 A \to \infty$ (high defect density): $$ \lim_{D_0 A \to \infty} Y = 0 \quad \text{(all models)} $$ ## 5. Critical Area Analysis ### 5.1 Definition Not all chip area is equally vulnerable. **Critical area** $A_c$ is the region where a defect of size $d$ causes circuit failure. $$ A_c(d) = \int_{\text{layout}} \mathbf{1}\left[\text{defect at } (x,y) \text{ with size } d \text{ causes failure}\right] \, dx \, dy $$ ### 5.2 Critical Area for Shorts For two parallel conductors with: - Length: $L$ - Spacing: $S$ $$ A_c^{\text{short}}(d) = \begin{cases} 2L(d - S) & \text{if } d > S \\ 0 & \text{if } d \leq S \end{cases} $$ ### 5.3 Critical Area for Opens For a conductor with: - Width: $W$ - Length: $L$ $$ A_c^{\text{open}}(d) = \begin{cases} L(d - W) & \text{if } d > W \\ 0 & \text{if } d \leq W \end{cases} $$ ### 5.4 Total Critical Area Integrate over the defect size distribution $f(d)$: $$ A_c = \int_0^\infty A_c(d) \cdot f(d) \, dd $$ ### 5.5 Defect Size Distribution Typically modeled as **power-law**: $$ f(d) = C \cdot d^{-p} \quad \text{for } d \geq d_{\min} $$ **Typical values:** - Exponent: $p \approx 2-4$ - Normalization constant: $C = (p-1) \cdot d_{\min}^{p-1}$ **Alternative: Log-normal distribution** (common for particle contamination): $$ f(d) = \frac{1}{d \sigma \sqrt{2\pi}} \exp\left(-\frac{(\ln d - \mu)^2}{2\sigma^2}\right) $$ ## 6. Multi-Layer Yield Modeling ### 6.1 Modern IC Structure Modern integrated circuits have **10-15+ metal layers**. Each layer $i$ has: - Defect density: $D_i$ - Critical area: $A_{c,i}$ - Clustering parameter: $\alpha_i$ (for Negative Binomial) ### 6.2 Poisson Multi-Layer Yield $$ Y_{\text{total}} = \prod_{i=1}^{n} Y_i = \prod_{i=1}^{n} e^{-D_i A_{c,i}} $$ Simplified form: $$ \boxed{Y_{\text{total}} = \exp\left(-\sum_{i=1}^{n} D_i A_{c,i}\right)} $$ ### 6.3 Negative Binomial Multi-Layer Yield $$ \boxed{Y_{\text{total}} = \prod_{i=1}^{n} \left(1 + \frac{D_i A_{c,i}}{\alpha_i}\right)^{-\alpha_i}} $$ ### 6.4 Log-Yield Decomposition Taking logarithms for analysis: $$ \ln Y_{\text{total}} = -\sum_{i=1}^{n} D_i A_{c,i} \quad \text{(Poisson)} $$ $$ \ln Y_{\text{total}} = -\sum_{i=1}^{n} \alpha_i \ln\left(1 + \frac{D_i A_{c,i}}{\alpha_i}\right) \quad \text{(Negative Binomial)} $$ ## 7. Spatial Point Process Formulation ### 7.1 Inhomogeneous Poisson Process Intensity function $\lambda(x, y)$ varies spatially across the wafer: $$ P(k \text{ defects in region } R) = \frac{\Lambda(R)^k e^{-\Lambda(R)}}{k!} $$ where the integrated intensity is: $$ \Lambda(R) = \iint_R \lambda(x,y) \, dx \, dy $$ ### 7.2 Cox Process (Doubly Stochastic) The intensity $\lambda(x,y)$ is itself a **random field**: $$ \lambda(x,y) = \exp\left(\mu + Z(x,y)\right) $$ where: - $\mu$ — Baseline log-intensity - $Z(x,y)$ — Gaussian random field with spatial correlation function $\rho(h)$ **Correlation structure:** $$ \text{Cov}(Z(x_1, y_1), Z(x_2, y_2)) = \sigma^2 \rho(h) $$ where $h = \sqrt{(x_2-x_1)^2 + (y_2-y_1)^2}$ ### 7.3 Neyman Type A (Cluster Process) Models defects occurring in clusters: 1. **Cluster centers:** Poisson process with intensity $\lambda_c$ 2. **Defects per cluster:** Poisson with mean $\mu$ 3. **Defect positions:** Scattered around cluster center (e.g., isotropic Gaussian) **Probability generating function:** $$ G(s) = \exp\left[\lambda_c A \left(e^{\mu(s-1)} - 1\right)\right] $$ **Mean and variance:** $$ E[N] = \lambda_c A \mu $$ $$ \text{Var}(N) = \lambda_c A \mu (1 + \mu) $$ ## 8. Statistical Estimation Methods ### 8.1 Maximum Likelihood Estimation #### 8.1.1 Data Structure Given: - $n$ chips with areas $A_1, A_2, \ldots, A_n$ - Binary outcomes $y_i \in \{0, 1\}$ (pass/fail) #### 8.1.2 Likelihood Function $$ \mathcal{L}(D_0, \alpha) = \prod_{i=1}^n Y_i^{y_i} (1 - Y_i)^{1-y_i} $$ where $Y_i = \left(1 + \frac{D_0 A_i}{\alpha}\right)^{-\alpha}$ #### 8.1.3 Log-Likelihood $$ \ell(D_0, \alpha) = \sum_{i=1}^n \left[y_i \ln Y_i + (1-y_i) \ln(1-Y_i)\right] $$ #### 8.1.4 Score Equations $$ \frac{\partial \ell}{\partial D_0} = 0, \quad \frac{\partial \ell}{\partial \alpha} = 0 $$ > **Note:** Requires numerical optimization (Newton-Raphson, BFGS, or EM algorithm). ### 8.2 Bayesian Estimation #### 8.2.1 Prior Distribution $$ D_0 \sim \text{Gamma}(a, b) $$ $$ \pi(D_0) = \frac{b^a}{\Gamma(a)} D_0^{a-1} e^{-b D_0} $$ #### 8.2.2 Posterior Distribution Given defect count $k$ on area $A$: $$ D_0 \mid k \sim \text{Gamma}(a + k, b + A) $$ **Posterior mean:** $$ \hat{D}_0 = \frac{a + k}{b + A} $$ **Posterior variance:** $$ \text{Var}(D_0 \mid k) = \frac{a + k}{(b + A)^2} $$ #### 8.2.3 Sequential Updating Bayesian framework enables sequential learning: $$ \text{Prior}_n \xrightarrow{\text{data } k_n} \text{Posterior}_n = \text{Prior}_{n+1} $$ ## 9. Statistical Process Control ### 9.1 c-Chart (Defect Counts) For **constant inspection area**: - **Center line:** $\bar{c}$ (average defect count) - **Upper Control Limit (UCL):** $\bar{c} + 3\sqrt{\bar{c}}$ - **Lower Control Limit (LCL):** $\max(0, \bar{c} - 3\sqrt{\bar{c}})$ ### 9.2 u-Chart (Defects per Unit Area) For **variable inspection area** $n_i$: $$ u_i = \frac{c_i}{n_i} $$ - **Center line:** $\bar{u}$ - **Control limits:** $\bar{u} \pm 3\sqrt{\frac{\bar{u}}{n_i}}$ ### 9.3 Overdispersion-Adjusted Charts For clustered defects (Negative Binomial), inflate the variance: $$ \text{UCL} = \bar{c} + 3\sqrt{\bar{c}\left(1 + \frac{\bar{c}}{\alpha}\right)} $$ $$ \text{LCL} = \max\left(0, \bar{c} - 3\sqrt{\bar{c}\left(1 + \frac{\bar{c}}{\alpha}\right)}\right) $$ ### 9.4 CUSUM Chart Cumulative sum for detecting small persistent shifts: $$ C_t^+ = \max(0, C_{t-1}^+ + (x_t - \mu_0 - K)) $$ $$ C_t^- = \max(0, C_{t-1}^- - (x_t - \mu_0 + K)) $$ where: - $K$ — Slack value (typically $0.5\sigma$) - Signal when $C_t^+$ or $C_t^-$ exceeds threshold $H$ ## 10. EUV Lithography Stochastic Effects ### 10.1 Photon Shot Noise At extreme ultraviolet wavelength (13.5 nm), **photon shot noise** becomes critical. Number of photons absorbed in resist volume $V$: $$ N \sim \text{Poisson}(\Phi \cdot \sigma \cdot V) $$ where: - $\Phi$ — Photon fluence (photons/area) - $\sigma$ — Absorption cross-section - $V$ — Resist volume ### 10.2 Line Edge Roughness (LER) Stochastic photon absorption causes spatial variation in resist exposure: $$ \sigma_{\text{LER}} \propto \frac{1}{\sqrt{\Phi \cdot V}} $$ **Critical Design Rule:** $$ \text{LER}_{3\sigma} < 0.1 \times \text{CD} $$ where CD = Critical Dimension (feature size) ### 10.3 Stochastic Printing Failures Probability of insufficient photons in a critical volume: $$ P(\text{failure}) = P(N < N_{\text{threshold}}) = \sum_{k=0}^{N_{\text{threshold}}-1} \frac{\lambda^k e^{-\lambda}}{k!} $$ where $\lambda = \Phi \sigma V$ ## 11. Reliability and Latent Defects ### 11.1 Defect Classification Not all defects cause immediate failure: - **Killer defects:** Cause immediate functional failure - **Latent defects:** May cause reliability failures over time $$ \lambda_{\text{total}} = \lambda_{\text{killer}} + \lambda_{\text{latent}} $$ ### 11.2 Yield vs. Reliability **Initial Yield:** $$ Y = e^{-\lambda_{\text{killer}} \cdot A} $$ **Reliability Function:** $$ R(t) = e^{-\lambda_{\text{latent}} \cdot A \cdot H(t)} $$ where $H(t)$ is the cumulative hazard function for latent defect activation. ### 11.3 Weibull Activation Model $$ H(t) = \left(\frac{t}{\eta}\right)^\beta $$ **Parameters:** - $\eta$ — Scale parameter (characteristic life) - $\beta$ — Shape parameter - $\beta < 1$: Decreasing failure rate (infant mortality) - $\beta = 1$: Constant failure rate (exponential) - $\beta > 1$: Increasing failure rate (wear-out) ## 12. Complete Mathematical Framework ### 12.1 Hierarchical Model Structure ``` - ┌─────────────────────────────────────────────────────────────┐ │ SEMICONDUCTOR YIELD MODEL HIERARCHY │ ├─────────────────────────────────────────────────────────────┤ │ │ │ Layer 1: DEFECT PHYSICS │ │ • Particle contamination │ │ • Process variation │ │ • Stochastic effects (EUV) │ │ ↓ │ │ Layer 2: SPATIAL POINT PROCESS │ │ • Inhomogeneous Poisson / Cox process │ │ • Defect size distribution: f(d) ∝ d^(-p) │ │ ↓ │ │ Layer 3: CRITICAL AREA CALCULATION │ │ • Layout-dependent geometry │ │ • Ac = ∫ Ac(d)$\cdot$f(d) dd │ │ ↓ │ │ Layer 4: YIELD MODEL │ │ • Y = (1 + D₀Ac/α)^(-α) │ │ • Multi-layer: Y = ∏ Yᵢ │ │ ↓ │ │ Layer 5: STATISTICAL INFERENCE │ │ • MLE / Bayesian estimation │ │ • SPC monitoring │ │ │ └─────────────────────────────────────────────────────────────┘ ``` ### 12.2 Summary of Key Equations | Concept | Equation | |---------|----------| | Poisson PMF | $P(X=k) = \frac{\lambda^k e^{-\lambda}}{k!}$ | | Simple Yield | $Y = e^{-D_0 A}$ | | Negative Binomial Yield | $Y = \left(1 + \frac{D_0 A}{\alpha}\right)^{-\alpha}$ | | Multi-Layer Yield | $Y = \prod_i \left(1 + \frac{D_i A_{c,i}}{\alpha_i}\right)^{-\alpha_i}$ | | Critical Area (shorts) | $A_c^{\text{short}}(d) = 2L(d-S)$ for $d > S$ | | Defect Size Distribution | $f(d) \propto d^{-p}$, $p \approx 2-4$ | | Bayesian Posterior | $D_0 \mid k \sim \text{Gamma}(a+k, b+A)$ | | Control Limits | $\bar{c} \pm 3\sqrt{\bar{c}(1 + \bar{c}/\alpha)}$ | | LER Scaling | $\sigma_{\text{LER}} \propto (\Phi V)^{-1/2}$ | ### 12.3 Typical Parameter Values | Parameter | Typical Range | Units | |-----------|---------------|-------| | Defect density $D_0$ | 0.01 - 1.0 | defects/cm² | | Clustering parameter $\alpha$ | 0.5 - 5 | dimensionless | | Defect size exponent $p$ | 2 - 4 | dimensionless | | Chip area $A$ | 1 - 800 | mm² |
Poisson yield model assumes random defects with constant density predicting yield as exponential of negative defect area product.
Simple yield model assuming random defects.
Poka-yoke examples include guide pins
Poka-yoke are error-proofing devices preventing mistakes or making them immediately obvious.
Error-proofing mechanisms.
Study crystal symmetry with polarized light.
Channel and spatial attention decoupled.
Polars is fast DataFrame library in Rust. Parallel, memory-efficient. Pandas alternative for big data.
Applies pressure to wafer during CMP.
Generate polite language.
Polish polysilicon for gate planarization.
Dummy poly for density uniformity.
Multiple query representations matched against document.
Polycrystalline silicon-germanium gates reduce polysilicon depletion and enable work function tuning.
Deposit polycrystalline silicon for gates and interconnects.
Average weights with exponential decay.
PolyCoder is open source code model. CMU. Trained on code corpus.
Polyhedral optimization uses mathematical frameworks for aggressive loop nest transformations.
Use polyimide adhesive.
Predict polymer characteristics.
Polynomial regression fits curved relationships using higher-order terms.
Polynomial features create interactions. Capture nonlinear relationships.
Neurons responding to multiple concepts.
Poly layer on backside for gettering.
Learn how long to compute for each input.
Replace attention with pooling.
Use pooling instead of attention.
Aggregate sets with attention.
Popcorning analysis investigates package delamination from rapid moisture vaporization during reflow.
Moisture-induced delamination.
Over-recommending popular items.
Popularity debiasing corrects for tendency of models to over-recommend popular items.
Population-based NAS maintains diverse architecture populations selecting promising candidates over generations.
Measure porosity of low-k dielectrics.
Low-k with nanoscale pores.
Structure-preserving networks for physical systems.
Portkey is AI gateway with observability. Fallbacks, caching, logging.
Artistic rendering of faces.
Pose conditioning guides generation using human pose keypoints.