listwise ranking,machine learning
Optimize entire ranked list.
169 technical terms and definitions
Optimize entire ranked list.
LiteLLM provides unified interface to many LLM providers. Drop-in replacement.
# Semiconductor Manufacturing Process: Lithography Mathematical Modeling ## 1. Introduction Lithography is the critical patterning step in semiconductor manufacturing that transfers circuit designs onto silicon wafers. It is essentially the "printing press" of chip making and determines the minimum feature sizes achievable. ### 1.1 Basic Process Flow 1. Coat wafer with photoresist 2. Expose photoresist to light through a mask/reticle 3. Develop the photoresist (remove exposed or unexposed regions) 4. Etch or deposit through the patterned resist 5. Strip the remaining resist ### 1.2 Types of Lithography - **Optical lithography:** DUV at 193nm, EUV at 13.5nm - **Electron beam lithography:** Direct-write, maskless - **Nanoimprint lithography:** Mechanical pattern transfer - **X-ray lithography:** Short wavelength exposure ## 2. Optical Image Formation The foundation of lithography modeling is **partially coherent imaging theory**, formalized through the Hopkins integral. ### 2.1 Hopkins Integral The intensity distribution at the image plane is given by: $$ I(x,y) = \iiint\!\!\!\int TCC(f_1,g_1;f_2,g_2) \cdot \tilde{M}(f_1,g_1) \cdot \tilde{M}^*(f_2,g_2) \cdot e^{2\pi i[(f_1-f_2)x + (g_1-g_2)y]} \, df_1\,dg_1\,df_2\,dg_2 $$ Where: - $I(x,y)$ — Intensity at image plane coordinates $(x,y)$ - $\tilde{M}(f,g)$ — Fourier transform of the mask transmission function - $TCC$ — Transmission Cross Coefficient ### 2.2 Transmission Cross Coefficient (TCC) The TCC encodes both the illumination source and lens pupil: $$ TCC(f_1,g_1;f_2,g_2) = \iint S(f,g) \cdot P(f+f_1,g+g_1) \cdot P^*(f+f_2,g+g_2) \, df\,dg $$ Where: - $S(f,g)$ — Source intensity distribution - $P(f,g)$ — Pupil function (encodes aberrations, NA cutoff) - $P^*$ — Complex conjugate of the pupil function ### 2.3 Sum of Coherent Systems (SOCS) To accelerate computation, the TCC is decomposed using eigendecomposition: $$ TCC(f_1,g_1;f_2,g_2) = \sum_{k=1}^{N} \lambda_k \cdot \phi_k(f_1,g_1) \cdot \phi_k^*(f_2,g_2) $$ The image becomes a weighted sum of coherent images: $$ I(x,y) = \sum_{k=1}^{N} \lambda_k \left| \mathcal{F}^{-1}\{\phi_k \cdot \tilde{M}\} \right|^2 $$ ### 2.4 Coherence Factor The partial coherence factor $\sigma$ is defined as: $$ \sigma = \frac{NA_{source}}{NA_{lens}} $$ - $\sigma = 0$ — Fully coherent illumination - $\sigma = 1$ — Matched illumination - $\sigma > 1$ — Overfilled illumination ## 3. Resolution Limits and Scaling Laws ### 3.1 Rayleigh Criterion The minimum resolvable feature size: $$ R = k_1 \frac{\lambda}{NA} $$ Where: - $R$ — Minimum resolvable feature - $k_1$ — Process factor (theoretical limit $\approx 0.25$, practical $\approx 0.3\text{--}0.4$) - $\lambda$ — Wavelength of light - $NA$ — Numerical aperture $= n \sin\theta$ ### 3.2 Depth of Focus $$ DOF = k_2 \frac{\lambda}{NA^2} $$ Where: - $DOF$ — Depth of focus - $k_2$ — Process-dependent constant ### 3.3 Technology Comparison | Technology | $\lambda$ (nm) | NA | Min. Feature | DOF | |:-----------|:---------------|:-----|:-------------|:----| | DUV ArF | 193 | 1.35 | ~38 nm | ~100 nm | | EUV | 13.5 | 0.33 | ~13 nm | ~120 nm | | High-NA EUV | 13.5 | 0.55 | ~8 nm | ~45 nm | ### 3.4 Resolution Enhancement Techniques (RETs) Key techniques to reduce effective $k_1$: - **Off-Axis Illumination (OAI):** Dipole, quadrupole, annular - **Phase-Shift Masks (PSM):** Alternating, attenuated - **Optical Proximity Correction (OPC):** Bias, serifs, sub-resolution assist features (SRAFs) - **Multiple Patterning:** LELE, SADP, SAQP ## 4. Rigorous Electromagnetic Mask Modeling ### 4.1 Thin Mask Approximation (Kirchhoff) For features much larger than wavelength: $$ E_{mask}(x,y) = t(x,y) \cdot E_{incident} $$ Where $t(x,y)$ is the complex transmission function. ### 4.2 Maxwell's Equations For sub-wavelength features, we must solve Maxwell's equations rigorously: $$ \nabla \times \mathbf{E} = -\frac{\partial \mathbf{B}}{\partial t} $$ $$ \nabla \times \mathbf{H} = \mathbf{J} + \frac{\partial \mathbf{D}}{\partial t} $$ ### 4.3 RCWA (Rigorous Coupled-Wave Analysis) For periodic structures with grating period $d$, fields are expanded in Floquet modes: $$ E(x,z) = \sum_{n=-N}^{N} A_n(z) \cdot e^{i k_{xn} x} $$ Where the wavevector components are: $$ k_{xn} = k_0 \sin\theta_0 + \frac{2\pi n}{d} $$ This yields a matrix eigenvalue problem: $$ \frac{d^2}{dz^2}\mathbf{A} = \mathbf{K}^2 \mathbf{A} $$ Where $\mathbf{K}$ couples different diffraction orders through the dielectric tensor. ### 4.4 FDTD (Finite-Difference Time-Domain) Discretizing Maxwell's equations on a Yee grid: $$ \frac{\partial H_y}{\partial t} = \frac{1}{\mu}\left(\frac{\partial E_x}{\partial z} - \frac{\partial E_z}{\partial x}\right) $$ $$ \frac{\partial E_x}{\partial t} = \frac{1}{\epsilon}\left(\frac{\partial H_y}{\partial z} - J_x\right) $$ ### 4.5 EUV Mask 3D Effects Shadowing from absorber thickness $h$ at angle $\theta$: $$ \Delta x = h \tan\theta $$ For EUV at 6° chief ray angle: $$ \Delta x \approx 0.105 \cdot h $$ ## 5. Photoresist Modeling ### 5.1 Dill ABC Model (Exposure) The photoactive compound (PAC) concentration evolves as: $$ \frac{\partial M(z,t)}{\partial t} = -I(z,t) \cdot M(z,t) \cdot C $$ Light absorption follows Beer-Lambert law: $$ \frac{dI}{dz} = -\alpha(M) \cdot I $$ $$ \alpha(M) = A \cdot M + B $$ Where: - $A$ — Bleachable absorption coefficient - $B$ — Non-bleachable absorption coefficient - $C$ — Exposure rate constant (quantum efficiency) - $M$ — Normalized PAC concentration ### 5.2 Post-Exposure Bake (PEB) — Reaction-Diffusion For chemically amplified resists (CARs): $$ \frac{\partial h}{\partial t} = D \nabla^2 h + k \cdot h \cdot M_{blocking} $$ Where: - $h$ — Acid concentration - $D$ — Diffusion coefficient - $k$ — Reaction rate constant - $M_{blocking}$ — Blocking group concentration The blocking group deprotection: $$ \frac{\partial M_{blocking}}{\partial t} = -k_{amp} \cdot h \cdot M_{blocking} $$ ### 5.3 Mack Development Rate Model $$ r(m) = r_{max} \cdot \frac{(a+1)(1-m)^n}{a + (1-m)^n} + r_{min} $$ Where: - $r$ — Development rate - $m$ — Normalized PAC concentration remaining - $n$ — Contrast (dissolution selectivity) - $a$ — Inhibition depth - $r_{max}$ — Maximum development rate (fully exposed) - $r_{min}$ — Minimum development rate (unexposed) ### 5.4 Enhanced Mack Model Including surface inhibition: $$ r(m,z) = r_{max} \cdot \frac{(a+1)(1-m)^n}{a + (1-m)^n} \cdot \left(1 - e^{-z/l}\right) + r_{min} $$ Where $l$ is the surface inhibition depth. ## 6. Optical Proximity Correction (OPC) ### 6.1 Forward Problem Given mask $M$, compute the printed wafer image: $$ I = F(M) $$ Where $F$ represents the complete optical and resist model. ### 6.2 Inverse Problem Given target pattern $T$, find mask $M$ such that: $$ F(M) \approx T $$ ### 6.3 Edge Placement Error (EPE) $$ EPE_i = x_{printed,i} - x_{target,i} $$ ### 6.4 OPC Optimization Formulation Minimize the cost function: $$ \mathcal{L}(M) = \sum_{i=1}^{N} w_i \cdot EPE_i^2 + \lambda \cdot R(M) $$ Where: - $w_i$ — Weight for evaluation point $i$ - $R(M)$ — Regularization term for mask manufacturability - $\lambda$ — Regularization strength ### 6.5 Gradient-Based OPC Using gradient descent: $$ M_{n+1} = M_n - \eta \frac{\partial \mathcal{L}}{\partial M} $$ The gradient requires computing: $$ \frac{\partial \mathcal{L}}{\partial M} = \sum_i 2 w_i \cdot EPE_i \cdot \frac{\partial EPE_i}{\partial M} + \lambda \frac{\partial R}{\partial M} $$ ### 6.6 Adjoint Method for Gradient Computation The sensitivity $\frac{\partial I}{\partial M}$ is computed efficiently using the adjoint formulation: $$ \frac{\partial \mathcal{L}}{\partial M} = \text{Re}\left\{ \tilde{M}^* \cdot \mathcal{F}\left\{ \sum_k \lambda_k \phi_k^* \cdot \mathcal{F}^{-1}\left\{ \phi_k \cdot \frac{\partial \mathcal{L}}{\partial I} \right\} \right\} \right\} $$ This avoids computing individual sensitivities for each mask pixel. ### 6.7 Mask Manufacturability Constraints Common regularization terms: - **Minimum feature size:** $R_1(M) = \sum \max(0, w_{min} - w_i)^2$ - **Minimum space:** $R_2(M) = \sum \max(0, s_{min} - s_i)^2$ - **Edge curvature:** $R_3(M) = \int |\kappa(s)|^2 ds$ - **Shot count:** $R_4(M) = N_{vertices}$ ## 7. Source-Mask Optimization (SMO) ### 7.1 Joint Optimization Formulation $$ \min_{S,M} \sum_{\text{patterns}} \|I(S,M) - T\|^2 + \lambda_S R_S(S) + \lambda_M R_M(M) $$ Where: - $S$ — Source intensity distribution - $M$ — Mask transmission function - $T$ — Target pattern - $R_S(S)$ — Source manufacturability regularization - $R_M(M)$ — Mask manufacturability regularization ### 7.2 Source Parameterization Pixelated source with constraints: $$ S(f,g) = \sum_{i,j} s_{ij} \cdot \text{rect}\left(\frac{f - f_i}{\Delta f}\right) \cdot \text{rect}\left(\frac{g - g_j}{\Delta g}\right) $$ Subject to: $$ 0 \leq s_{ij} \leq 1 \quad \forall i,j $$ $$ \sum_{i,j} s_{ij} = S_{total} $$ ### 7.3 Alternating Optimization **Algorithm:** 1. Initialize $S_0$, $M_0$ 2. For iteration $n = 1, 2, \ldots$: - Fix $S_n$, optimize $M_{n+1} = \arg\min_M \mathcal{L}(S_n, M)$ - Fix $M_{n+1}$, optimize $S_{n+1} = \arg\min_S \mathcal{L}(S, M_{n+1})$ 3. Repeat until convergence ### 7.4 Gradient Computation for SMO Source gradient: $$ \frac{\partial I}{\partial S}(x,y) = \left| \mathcal{F}^{-1}\{P \cdot \tilde{M}\}(x,y) \right|^2 $$ Mask gradient uses the adjoint method as in OPC. ## 8. Stochastic Effects and EUV ### 8.1 Photon Shot Noise Photon counts follow a Poisson distribution: $$ P(n) = \frac{\bar{n}^n e^{-\bar{n}}}{n!} $$ For EUV at 13.5 nm, photon energy is: $$ E_{photon} = \frac{hc}{\lambda} = \frac{1240 \text{ eV} \cdot \text{nm}}{13.5 \text{ nm}} \approx 92 \text{ eV} $$ Mean photons per pixel: $$ \bar{n} = \frac{\text{Dose} \cdot A_{pixel}}{E_{photon}} $$ ### 8.2 Relative Shot Noise $$ \frac{\sigma_n}{\bar{n}} = \frac{1}{\sqrt{\bar{n}}} $$ For 30 mJ/cm² dose and 10 nm pixel: $$ \bar{n} \approx 200 \text{ photons} \implies \sigma/\bar{n} \approx 7\% $$ ### 8.3 Line Edge Roughness (LER) Characterized by power spectral density: $$ PSD(f) = \frac{LER^2 \cdot \xi}{1 + (2\pi f \xi)^{2(1+H)}} $$ Where: - $LER$ — RMS line edge roughness (3σ value) - $\xi$ — Correlation length - $H$ — Hurst exponent (0 < H < 1) - $f$ — Spatial frequency ### 8.4 LER Decomposition $$ LER^2 = LWR^2/2 + \sigma_{placement}^2 $$ Where: - $LWR$ — Line width roughness - $\sigma_{placement}$ — Line placement error ### 8.5 Stochastic Defectivity Probability of printing failure (e.g., missing contact): $$ P_{fail} = 1 - \prod_{i} \left(1 - P_{fail,i}\right) $$ For a chip with $10^{10}$ contacts at 99.9999999% yield per contact: $$ P_{chip,fail} \approx 1\% $$ ### 8.6 Monte Carlo Simulation Steps 1. **Photon absorption:** Generate random events $\sim \text{Poisson}(\bar{n})$ 2. **Acid generation:** Each photon generates acid at random location 3. **Diffusion:** Brownian motion during PEB: $\langle r^2 \rangle = 6Dt$ 4. **Deprotection:** Local reaction based on acid concentration 5. **Development:** Cellular automata or level-set method ## 9. Multiple Patterning Mathematics ### 9.1 Graph Coloring Formulation When pitch $< \lambda/(2NA)$, single-exposure patterning fails. **Graph construction:** - Nodes $V$ = features (polygons) - Edges $E$ = spacing conflicts (features too close for one mask) - Colors $C$ = different masks ### 9.2 k-Colorability Problem Find assignment $c: V \rightarrow \{1, 2, \ldots, k\}$ such that: $$ c(u) \neq c(v) \quad \forall (u,v) \in E $$ This is **NP-complete** for $k \geq 3$. ### 9.3 Integer Linear Programming (ILP) Formulation Binary variables: $x_{v,c} \in \{0,1\}$ (node $v$ assigned color $c$) **Objective:** $$ \min \sum_{(u,v) \in E} \sum_c x_{u,c} \cdot x_{v,c} \cdot w_{uv} $$ **Constraints:** $$ \sum_{c=1}^{k} x_{v,c} = 1 \quad \forall v \in V $$ $$ x_{u,c} + x_{v,c} \leq 1 \quad \forall (u,v) \in E, \forall c $$ ### 9.4 Self-Aligned Multiple Patterning (SADP) Spacer pitch after $n$ iterations: $$ p_n = \frac{p_0}{2^n} $$ Where $p_0$ is the initial (lithographic) pitch. ## 10. Process Control Mathematics ### 10.1 Overlay Control Polynomial model across the wafer: $$ OVL_x(x,y) = a_0 + a_1 x + a_2 y + a_3 xy + a_4 x^2 + a_5 y^2 + \ldots $$ **Physical interpretation:** | Coefficient | Physical Effect | |:------------|:----------------| | $a_0$ | Translation | | $a_1$, $a_2$ | Scale (magnification) | | $a_3$ | Rotation | | $a_4$, $a_5$ | Non-orthogonality | ### 10.2 Overlay Correction Least squares fitting: $$ \mathbf{a} = (\mathbf{X}^T \mathbf{X})^{-1} \mathbf{X}^T \mathbf{y} $$ Where $\mathbf{X}$ is the design matrix and $\mathbf{y}$ is measured overlay. ### 10.3 Run-to-Run Control — EWMA Exponentially Weighted Moving Average: $$ \hat{y}_{n+1} = \lambda y_n + (1-\lambda)\hat{y}_n $$ Where: - $\hat{y}_{n+1}$ — Predicted output - $y_n$ — Measured output at step $n$ - $\lambda$ — Smoothing factor $(0 < \lambda < 1)$ ### 10.4 CDU Variance Decomposition $$ \sigma^2_{total} = \sigma^2_{local} + \sigma^2_{field} + \sigma^2_{wafer} + \sigma^2_{lot} $$ **Sources:** - **Local:** Shot noise, LER, resist - **Field:** Lens aberrations, mask - **Wafer:** Focus/dose uniformity - **Lot:** Tool-to-tool variation ### 10.5 Process Capability Index $$ C_{pk} = \min\left(\frac{USL - \mu}{3\sigma}, \frac{\mu - LSL}{3\sigma}\right) $$ Where: - $USL$, $LSL$ — Upper/lower specification limits - $\mu$ — Process mean - $\sigma$ — Process standard deviation ## 11. Machine Learning Integration ### 11.1 Applications Overview | Application | Method | Purpose | |:------------|:-------|:--------| | Hotspot detection | CNNs | Predict yield-limiting patterns | | OPC acceleration | Neural surrogates | Replace expensive physics sims | | Metrology | Regression models | Virtual measurements | | Defect classification | Image classifiers | Automated inspection | | Etch prediction | Physics-informed NN | Predict etch profiles | ### 11.2 Neural Network Surrogate Model A neural network approximates the forward model: $$ \hat{I}(x,y) = f_{NN}(\text{mask}, \text{source}, \text{focus}, \text{dose}; \theta) $$ Training objective: $$ \theta^* = \arg\min_\theta \sum_{i=1}^{N} \|f_{NN}(M_i; \theta) - I_i^{rigorous}\|^2 $$ ### 11.3 Hotspot Detection with CNNs Binary classification: $$ P(\text{hotspot} | \text{pattern}) = \sigma(\mathbf{W} \cdot \mathbf{features} + b) $$ Where $\sigma$ is the sigmoid function and features are extracted by convolutional layers. ### 11.4 Inverse Lithography with Deep Learning Generator network $G$ maps target to mask: $$ \hat{M} = G(T; \theta_G) $$ Training with physics-based loss: $$ \mathcal{L} = \|F(G(T)) - T\|^2 + \lambda \cdot R(G(T)) $$ ## 12. Mathematical Disciplines | Mathematical Domain | Application in Lithography | |:--------------------|:---------------------------| | **Fourier Optics** | Image formation, aberrations, frequency analysis | | **Electromagnetic Theory** | RCWA, FDTD, rigorous mask simulation | | **Partial Differential Equations** | Resist diffusion, development, reaction kinetics | | **Optimization Theory** | OPC, SMO, inverse problems, gradient descent | | **Probability & Statistics** | Shot noise, LER, SPC, process control | | **Linear Algebra** | Matrix methods, eigendecomposition, least squares | | **Graph Theory** | Multiple patterning decomposition, routing | | **Numerical Methods** | FEM, finite differences, Monte Carlo | | **Machine Learning** | Surrogate models, pattern recognition, CNNs | | **Signal Processing** | Image analysis, metrology, filtering | ## Key Equations Quick Reference ### Imaging $$ I(x,y) = \sum_{k} \lambda_k \left| \mathcal{F}^{-1}\{\phi_k \cdot \tilde{M}\} \right|^2 $$ ### Resolution $$ R = k_1 \frac{\lambda}{NA} $$ ### Depth of Focus $$ DOF = k_2 \frac{\lambda}{NA^2} $$ ### Development Rate $$ r(m) = r_{max} \cdot \frac{(a+1)(1-m)^n}{a + (1-m)^n} + r_{min} $$ ### LER Power Spectrum $$ PSD(f) = \frac{LER^2 \cdot \xi}{1 + (2\pi f \xi)^{2(1+H)}} $$ ### OPC Cost Function $$ \mathcal{L}(M) = \sum_{i} w_i \cdot EPE_i^2 + \lambda \cdot R(M) $$
Improved version of Llama with better safety and performance.
Meta's open-source foundation language model family.
LlamaIndex enables data-augmented LLM applications with retrieval and indexing.
Data framework for LLM applications focused on ingestion and retrieval.
LlamaIndex specializes in RAG and data connectors. Index various data sources.
Visual instruction tuning for LLMs.
LLM agents use language models to autonomously pursue goals through iterative planning and tool use.
LLM-as-judge uses strong model to evaluate weaker ones. Scales better than human eval, correlates reasonably.
Use strong LLM to evaluate other model outputs.
# LLM Mathematics Modeling
## 1. Mathematical Foundations of LLMs
### 1.1 Transformer Architecture Mathematics
The transformer architecture (Vaswani et al., 2017) consists of these core mathematical operations:
#### Self-Attention Mechanism
The scaled dot-product attention is defined as:
$$
\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V
$$
**Variable Definitions:**
- $Q$ — Query matrix: $Q = XW^Q$ where $W^Q \in \mathbb{R}^{d_{model} \times d_k}$
- $K$ — Key matrix: $K = XW^K$ where $W^K \in \mathbb{R}^{d_{model} \times d_k}$
- $V$ — Value matrix: $V = XW^V$ where $W^V \in \mathbb{R}^{d_{model} \times d_v}$
- $d_k$ — Dimension of key vectors (scaling factor prevents gradient vanishing)
- $\sqrt{d_k}$ — Scaling factor to normalize dot products
#### Multi-Head Attention
$$
\text{MultiHead}(Q, K, V) = \text{Concat}(\text{head}_1, ..., \text{head}_h)W^O
$$
Where each head is computed as:
$$
\text{head}_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)
$$
**Parameters:**
- $h$ — Number of attention heads (typically 8, 12, 32, or more)
- $W^O \in \mathbb{R}^{hd_v \times d_{model}}$ — Output projection matrix
#### Feed-Forward Networks (FFN)
Position-wise feed-forward network applied to each position:
$$
\text{FFN}(x) = \max(0, xW_1 + b_1)W_2 + b_2
$$
Or with GELU activation (more common in modern LLMs):
$$
\text{FFN}(x) = \text{GELU}(xW_1 + b_1)W_2 + b_2
$$
Where GELU is defined as:
$$
\text{GELU}(x) = x \cdot \Phi(x) = x \cdot \frac{1}{2}\left[1 + \text{erf}\left(\frac{x}{\sqrt{2}}\right)\right]
$$
#### Layer Normalization
$$
\text{LayerNorm}(x) = \gamma \cdot \frac{x - \mu}{\sigma + \epsilon} + \beta
$$
**Where:**
- $\mu = \frac{1}{d}\sum_{i=1}^{d} x_i$ — Mean across features
- $\sigma = \sqrt{\frac{1}{d}\sum_{i=1}^{d}(x_i - \mu)^2}$ — Standard deviation
- $\gamma, \beta$ — Learnable scale and shift parameters
- $\epsilon$ — Small constant for numerical stability (typically $10^{-5}$)
### 1.2 Statistical Language Modeling
#### Autoregressive Probability Model
LLMs estimate the conditional probability distribution:
$$
P(w_t | w_1, w_2, ..., w_{t-1}; \theta)
$$
The joint probability of a sequence factorizes as:
$$
P(w_1, w_2, ..., w_T) = \prod_{t=1}^{T} P(w_t | w_{
Query language for constraining LLM generation.
Ensure experts are used roughly equally to avoid underutilization.
Load balancing distributes work evenly preventing bottlenecks in agent systems.
Load balancing loss encourages uniform expert utilization.
Load shedding rejects requests when system capacity is exceeded preventing overload.
Local level model is simplest structural time series with stochastically evolving level component.
Perform multiple local updates before synchronizing.
Local trend model includes both stochastically evolving level and slope components for trending series.
Combine local sliding window with sparse global attention.
Locally typical sampling considers information content conditional on context.
Lock-in thermography applies periodic electrical stimulation and phase-sensitive thermal imaging to detect weak heat sources from intermittent defects.
Thermal imaging of defects.
Local Outlier Factor adapted for time series detects anomalies by comparing local densities in feature space.
Local Outlier Factor for time series detects anomalies by comparing densities in windowed feature space.
Logarithmic quantization represents values in log domain improving dynamic range.
Log-Gaussian Cox processes use Gaussian random fields to model spatially or temporally varying intensity functions.
Use logarithmic scale for quantization.
Use LLMs to interact with logic systems.
Logistics optimization determines efficient transportation routing warehousing and distribution strategies.
Logit bias adjusts token probabilities to encourage or discourage specific outputs.
Decode intermediate activations.
Models handling 100K+ tokens.
Long convolutions model extended dependencies through large kernel sizes.
Identify overly long methods.
Deal with prompts exceeding limit.
Long-tail recommendation focuses on effectively suggesting less popular items with few interactions.
Long-term memory stores experiences and knowledge for retrieval in future tasks.
Capture dependencies across many frames.
Combination of local and global attention.
Longformer combines local sliding window with global attention for efficient long context.
Model with local+global attention for long documents.
Lookahead decoding generates multiple future tokens simultaneously when possible.
Loop optimization reorders and transforms loops maximizing parallelism and data locality.
Loop unrolling replicates loop bodies reducing branching overhead and enabling instruction-level parallelism.
LoRA and DreamBooth customize diffusion models. Train on few images. Personalized generation.
Low-Rank Adaptation fine-tunes diffusion models efficiently by learning low-rank weight updates.
Efficient fine-tuning with low-rank adaptation.