reflection coefficient, signal & power integrity
Reflection coefficient quantifies signal reflection magnitude from impedance discontinuities.
reflection high-energy electron diffraction (rheed),reflection high-energy electron diffraction,rheed,metrology
In-situ surface crystallography.
reflection interferometry,metrology
Monitor etch depth using interference.
reflection prompting, prompting
Model reflects on and improves output.
reflection, prompting techniques
Reflection prompts models to critique and refine their own outputs iteratively.
reflection,self critique,refine
Reflection prompts model to critique its own output and refine. Iterative improvement without external feedback.
reflections,design
Signal bouncing due to impedance mismatch.
reflective optics (euv),reflective optics,euv,lithography
Mirrors instead of lenses for EUV light.
reflectometry,metrology
Measure film thickness from interference of reflected light.
reflexion,ai agent
Agent learns from feedback and mistakes by generating reflections and improving.
reflow profile, packaging
Temperature vs time during reflow.
reflow soldering for smt, packaging
Solder paste melted to attach.
reformer, llm architecture
Reformer uses locality-sensitive hashing for approximate attention matching.
reformer,foundation model
Use LSH attention to reduce complexity from quadratic to linear.
refusal behavior, ai safety
Model declining to answer.
refusal calibration, ai safety
Balance safety and helpfulness.
refusal training, ai safety
Refusal training explicitly teaches models when to decline requests.
refusal training, ai safety
Teach model when to refuse.
refusal,decline,cannot
Graceful refusal when model cannot or should not help. Explain limitation, suggest alternatives.
refused bequest, code ai
Subclass not using inherited methods.
regenerative thermal, environmental & sustainability
Regenerative thermal oxidizers recover heat through ceramic beds improving energy efficiency.
regex constraint, llm optimization
Regex constraints enforce pattern matching during text generation.
regex,pattern,generate
Generate regex from description. Complex patterns made easy.
region-based captioning, multimodal ai
Caption specific image areas.
register adaptation, nlp
Match language register.
register file, hardware
Fastest GPU memory.
register tokens, computer vision
Extra tokens improving ViT training stability.
regnet, computer vision
Design space for efficient networks.
regression analysis quality, quality & reliability
Regression analysis models relationships between response and predictor variables.
regression analysis,regression,ols,least squares,pls,partial least squares,ridge,lasso,semiconductor regression,process regression
# Regression Analysis
Semiconductor fabrication involves hundreds of sequential process steps, each governed by dozens of parameters. Regression analysis serves critical functions:
- Process Modeling: Understanding relationships between inputs and quality outputs
- Virtual Metrology: Predicting measurements from real-time sensor data
- Run-to-Run Control: Adaptive process adjustment
- Yield Optimization: Maximizing device performance and throughput
- Fault Detection: Identifying and diagnosing process excursions
Core Mathematical Framework
Ordinary Least Squares (OLS)
The foundational linear regression model:
$$
\mathbf{y} = \mathbf{X}\boldsymbol{\beta} + \boldsymbol{\varepsilon}
$$
Variable Definitions:
- $\mathbf{y}$ — $n \times 1$ response vector (e.g., film thickness, etch rate, yield)
- $\mathbf{X}$ — $n \times (k+1)$ design matrix of process parameters
- $\boldsymbol{\beta}$ — $(k+1) \times 1$ coefficient vector
- $\boldsymbol{\varepsilon} \sim N(\mathbf{0}, \sigma^2\mathbf{I})$ — error term
OLS Estimator:
$$
\hat{\boldsymbol{\beta}} = (\mathbf{X}^\top\mathbf{X})^{-1}\mathbf{X}^\top\mathbf{y}
$$
Variance-Covariance Matrix of Estimator:
$$
\text{Var}(\hat{\boldsymbol{\beta}}) = \sigma^2(\mathbf{X}^\top\mathbf{X})^{-1}
$$
Unbiased Variance Estimate:
$$
\hat{\sigma}^2 = \frac{\mathbf{e}^\top\mathbf{e}}{n - k - 1} = \frac{\sum_{i=1}^{n}(y_i - \hat{y}_i)^2}{n - k - 1}
$$
Response Surface Methodology (RSM)
Critical for semiconductor process optimization, RSM uses second-order polynomial models.
Second-Order Model
$$
y = \beta_0 + \sum_{i=1}^{k}\beta_i x_i + \sum_{i=1}^{k}\beta_{ii}x_i^2 + \sum_{i n$)
- Addresses multicollinearity
- Captures latent variable structures
- Simultaneously models X and Y relationships
NIPALS Algorithm
1. Initialize: $\mathbf{u} = \mathbf{y}$
2. X-weight:
$$\mathbf{w} = \frac{\mathbf{X}^\top\mathbf{u}}{\|\mathbf{X}^\top\mathbf{u}\|}$$
3. X-score:
$$\mathbf{t} = \mathbf{X}\mathbf{w}$$
4. Y-loading:
$$q = \frac{\mathbf{y}^\top\mathbf{t}}{\mathbf{t}^\top\mathbf{t}}$$
5. Y-score update:
$$\mathbf{u} = \frac{\mathbf{y}q}{q^2}$$
6. Iterate until convergence
7. Deflate X and Y, extract next component
Model Structure
$$
\mathbf{X} = \mathbf{T}\mathbf{P}^\top + \mathbf{E}
$$
$$
\mathbf{Y} = \mathbf{T}\mathbf{Q}^\top + \mathbf{F}
$$
Where:
- $\mathbf{T}$ — score matrix (latent variables)
- $\mathbf{P}$ — X-loadings
- $\mathbf{Q}$ — Y-loadings
- $\mathbf{E}, \mathbf{F}$ — residuals
Spatial Regression for Wafer Maps
Wafer-level variation exhibits spatial patterns requiring specialized models.
Zernike Polynomial Decomposition
General Form:
$$
Z(r,\theta) = \sum_{n,m} a_{nm} Z_n^m(r,\theta)
$$
Standard Zernike Polynomials (first few terms):
| Index | Name | Formula |
|-------|------|---------|
| $Z_0^0$ | Piston | $1$ |
| $Z_1^{-1}$ | Tilt Y | $r\sin\theta$ |
| $Z_1^{1}$ | Tilt X | $r\cos\theta$ |
| $Z_2^{-2}$ | Astigmatism 45° | $r^2\sin 2\theta$ |
| $Z_2^{0}$ | Defocus | $2r^2 - 1$ |
| $Z_2^{2}$ | Astigmatism 0° | $r^2\cos 2\theta$ |
| $Z_3^{-1}$ | Coma Y | $(3r^3 - 2r)\sin\theta$ |
| $Z_3^{1}$ | Coma X | $(3r^3 - 2r)\cos\theta$ |
| $Z_4^{0}$ | Spherical | $6r^4 - 6r^2 + 1$ |
Orthogonality Property:
$$
\int_0^1 \int_0^{2\pi} Z_n^m(r,\theta) Z_{n'}^{m'}(r,\theta) \, r \, dr \, d\theta = \frac{\pi}{n+1}\delta_{nn'}\delta_{mm'}
$$
Gaussian Process Regression (Kriging)
Prior Distribution:
$$
f(\mathbf{x}) \sim \mathcal{GP}(m(\mathbf{x}), k(\mathbf{x}, \mathbf{x}'))
$$
Common Kernel Functions:
*Squared Exponential (RBF)*:
$$
k(\mathbf{x}, \mathbf{x}') = \sigma^2 \exp\left(-\frac{\|\mathbf{x} - \mathbf{x}'\|^2}{2\ell^2}\right)
$$
*Matérn Kernel*:
$$
k(r) = \sigma^2 \frac{2^{1-\nu}}{\Gamma(\nu)}\left(\frac{\sqrt{2\nu}r}{\ell}\right)^\nu K_\nu\left(\frac{\sqrt{2\nu}r}{\ell}\right)
$$
Where $K_\nu$ is the modified Bessel function of the second kind.
Posterior Predictive Mean:
$$
\bar{f}_* = \mathbf{k}_*^\top(\mathbf{K} + \sigma_n^2\mathbf{I})^{-1}\mathbf{y}
$$
Posterior Predictive Variance:
$$
\text{Var}(f_*) = k(\mathbf{x}_*, \mathbf{x}_*) - \mathbf{k}_*^\top(\mathbf{K} + \sigma_n^2\mathbf{I})^{-1}\mathbf{k}_*
$$
Mixed Effects Models
Semiconductor data has hierarchical structure (wafers within lots, lots within tools).
General Model
$$
y_{ijk} = \mathbf{x}_{ijk}^\top\boldsymbol{\beta} + b_i^{(\text{tool})} + b_{ij}^{(\text{lot})} + \varepsilon_{ijk}
$$
Random Effects Distribution:
- $b_i^{(\text{tool})} \sim N(0, \sigma_{\text{tool}}^2)$
- $b_{ij}^{(\text{lot})} \sim N(0, \sigma_{\text{lot}}^2)$
- $\varepsilon_{ijk} \sim N(0, \sigma^2)$
Matrix Notation
$$
\mathbf{y} = \mathbf{X}\boldsymbol{\beta} + \mathbf{Z}\mathbf{b} + \boldsymbol{\varepsilon}
$$
Where:
- $\mathbf{b} \sim N(\mathbf{0}, \mathbf{G})$
- $\boldsymbol{\varepsilon} \sim N(\mathbf{0}, \mathbf{R})$
- $\text{Var}(\mathbf{y}) = \mathbf{V} = \mathbf{Z}\mathbf{G}\mathbf{Z}^\top + \mathbf{R}$
REML Estimation
Restricted Log-Likelihood:
$$
\ell_{\text{REML}}(\boldsymbol{\theta}) = -\frac{1}{2}\left[\log|\mathbf{V}| + \log|\mathbf{X}^\top\mathbf{V}^{-1}\mathbf{X}| + \mathbf{r}^\top\mathbf{V}^{-1}\mathbf{r}\right]
$$
Where $\mathbf{r} = \mathbf{y} - \mathbf{X}\hat{\boldsymbol{\beta}}$.
Physics-Informed Regression Models
Arrhenius-Based Models (Thermal Processes)
Rate Equation:
$$
k = A \exp\left(-\frac{E_a}{RT}\right)
$$
Linearized Form (for regression):
$$
\ln(k) = \ln(A) - \frac{E_a}{R} \cdot \frac{1}{T}
$$
Parameters:
- $k$ — rate constant
- $A$ — pre-exponential factor
- $E_a$ — activation energy (J/mol)
- $R$ — gas constant (8.314 J/mol·K)
- $T$ — absolute temperature (K)
Preston's Equation (CMP)
Basic Form:
$$
\text{MRR} = K_p \cdot P \cdot V
$$
Extended Model:
$$
\text{MRR} = K_p \cdot P^a \cdot V^b \cdot f(\text{slurry}, \text{pad})
$$
Where:
- MRR — material removal rate
- $K_p$ — Preston coefficient
- $P$ — applied pressure
- $V$ — relative velocity
Lithography Focus-Exposure Model
$$
\text{CD} = \beta_0 + \beta_1 E + \beta_2 F + \beta_3 E^2 + \beta_4 F^2 + \beta_5 EF + \varepsilon
$$
Variables:
- CD — critical dimension
- $E$ — exposure dose
- $F$ — focus offset
Bossung Curve: Plot of CD vs. focus at various exposure levels.
Virtual Metrology Mathematics
Predicting quality measurements from equipment sensor data in real-time.
Model Structure
$$
\hat{y} = f(\mathbf{x}_{\text{FDC}}; \boldsymbol{\theta})
$$
Where $\mathbf{x}_{\text{FDC}}$ is Fault Detection and Classification sensor data.
EWMA Run-to-Run Control
Exponentially Weighted Moving Average:
$$
\hat{T}_{n+1} = \lambda y_n + (1-\lambda)\hat{T}_n
$$
Properties:
- $\lambda \in (0,1]$ — smoothing parameter
- Smaller $\lambda$ → more smoothing
- Larger $\lambda$ → faster response to changes
Kalman Filter Approach
State Equation:
$$
\mathbf{x}_{k} = \mathbf{A}\mathbf{x}_{k-1} + \mathbf{w}_k, \quad \mathbf{w}_k \sim N(\mathbf{0}, \mathbf{Q})
$$
Measurement Equation:
$$
y_k = \mathbf{H}\mathbf{x}_k + v_k, \quad v_k \sim N(0, R)
$$
Update Equations:
*Predict*:
$$
\hat{\mathbf{x}}_{k|k-1} = \mathbf{A}\hat{\mathbf{x}}_{k-1|k-1}
$$
$$
\mathbf{P}_{k|k-1} = \mathbf{A}\mathbf{P}_{k-1|k-1}\mathbf{A}^\top + \mathbf{Q}
$$
*Update*:
$$
\mathbf{K}_k = \mathbf{P}_{k|k-1}\mathbf{H}^\top(\mathbf{H}\mathbf{P}_{k|k-1}\mathbf{H}^\top + R)^{-1}
$$
$$
\hat{\mathbf{x}}_{k|k} = \hat{\mathbf{x}}_{k|k-1} + \mathbf{K}_k(y_k - \mathbf{H}\hat{\mathbf{x}}_{k|k-1})
$$
Classification and Count Models
Logistic Regression (Binary Outcomes)
For pass/fail or defect/no-defect classification:
Model:
$$
P(Y=1|\mathbf{x}) = \frac{1}{1 + \exp(-\mathbf{x}^\top\boldsymbol{\beta})} = \sigma(\mathbf{x}^\top\boldsymbol{\beta})
$$
Logit Link:
$$
\text{logit}(p) = \ln\left(\frac{p}{1-p}\right) = \mathbf{x}^\top\boldsymbol{\beta}
$$
Log-Likelihood:
$$
\ell(\boldsymbol{\beta}) = \sum_{i=1}^{n}\left[y_i \log(\pi_i) + (1-y_i)\log(1-\pi_i)\right]
$$
Newton-Raphson Update:
$$
\boldsymbol{\beta}^{(t+1)} = \boldsymbol{\beta}^{(t)} + (\mathbf{X}^\top\mathbf{W}\mathbf{X})^{-1}\mathbf{X}^\top(\mathbf{y} - \boldsymbol{\pi})
$$
Where $\mathbf{W} = \text{diag}(\pi_i(1-\pi_i))$.
Poisson Regression (Defect Counts)
Model:
$$
\log(\mu) = \mathbf{x}^\top\boldsymbol{\beta}, \quad Y \sim \text{Poisson}(\mu)
$$
Probability Mass Function:
$$
P(Y = y) = \frac{\mu^y e^{-\mu}}{y!}
$$
Model Validation and Diagnostics
Goodness of Fit Metrics
Coefficient of Determination:
$$
R^2 = 1 - \frac{\text{SSE}}{\text{SST}} = 1 - \frac{\sum_{i=1}^{n}(y_i - \hat{y}_i)^2}{\sum_{i=1}^{n}(y_i - \bar{y})^2}
$$
Adjusted R-Squared:
$$
R^2_{\text{adj}} = 1 - (1-R^2)\frac{n-1}{n-k-1}
$$
Root Mean Square Error:
$$
\text{RMSE} = \sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_i - \hat{y}_i)^2}
$$
Mean Absolute Error:
$$
\text{MAE} = \frac{1}{n}\sum_{i=1}^{n}|y_i - \hat{y}_i|
$$
Cross-Validation
K-Fold CV Error:
$$
\text{CV}_{(K)} = \frac{1}{K}\sum_{k=1}^{K}\text{MSE}_k
$$
Leave-One-Out CV:
$$
\text{LOOCV} = \frac{1}{n}\sum_{i=1}^{n}(y_i - \hat{y}_{(-i)})^2
$$
Information Criteria
Akaike Information Criterion:
$$
\text{AIC} = 2k - 2\ln(\hat{L})
$$
Bayesian Information Criterion:
$$
\text{BIC} = k\ln(n) - 2\ln(\hat{L})
$$
Diagnostic Statistics
Variance Inflation Factor:
$$
\text{VIF}_j = \frac{1}{1-R_j^2}
$$
Where $R_j^2$ is the $R^2$ from regressing $x_j$ on all other predictors.
Rule of thumb: VIF > 10 indicates problematic multicollinearity.
Cook's Distance:
$$
D_i = \frac{(\hat{\mathbf{y}} - \hat{\mathbf{y}}_{(-i)})^\top(\hat{\mathbf{y}} - \hat{\mathbf{y}}_{(-i)})}{k \cdot \text{MSE}}
$$
Leverage:
$$
h_{ii} = [\mathbf{H}]_{ii}
$$
Where $\mathbf{H} = \mathbf{X}(\mathbf{X}^\top\mathbf{X})^{-1}\mathbf{X}^\top$ is the hat matrix.
Studentized Residuals:
$$
r_i = \frac{e_i}{\hat{\sigma}\sqrt{1 - h_{ii}}}
$$
Bayesian Regression
Provides full uncertainty quantification for risk-sensitive manufacturing decisions.
Bayesian Linear Regression
Prior:
$$
\boldsymbol{\beta} | \sigma^2 \sim N(\boldsymbol{\beta}_0, \sigma^2\mathbf{V}_0)
$$
$$
\sigma^2 \sim \text{Inverse-Gamma}(a_0, b_0)
$$
Posterior:
$$
\boldsymbol{\beta} | \mathbf{y}, \sigma^2 \sim N(\boldsymbol{\beta}_n, \sigma^2\mathbf{V}_n)
$$
Posterior Parameters:
$$
\mathbf{V}_n = (\mathbf{V}_0^{-1} + \mathbf{X}^\top\mathbf{X})^{-1}
$$
$$
\boldsymbol{\beta}_n = \mathbf{V}_n(\mathbf{V}_0^{-1}\boldsymbol{\beta}_0 + \mathbf{X}^\top\mathbf{y})
$$
Predictive Distribution
$$
p(y_*|\mathbf{x}_*, \mathbf{y}) = \int p(y_*|\mathbf{x}_*, \boldsymbol{\beta}, \sigma^2) \, p(\boldsymbol{\beta}, \sigma^2|\mathbf{y}) \, d\boldsymbol{\beta} \, d\sigma^2
$$
For conjugate priors, this is a Student-t distribution.
Credible Intervals
95% Credible Interval for $\beta_j$:
$$
\beta_j \in \left[\hat{\beta}_j - t_{0.025,\nu}\cdot \text{SE}(\hat{\beta}_j), \quad \hat{\beta}_j + t_{0.025,\nu}\cdot \text{SE}(\hat{\beta}_j)\right]
$$
Design of Experiments (DOE)
Full Factorial Design
For $k$ factors at 2 levels:
$$
N = 2^k \text{ runs}
$$
Fractional Factorial Design
$$
N = 2^{k-p} \text{ runs}
$$
Resolution:
- Resolution III: Main effects aliased with 2-factor interactions
- Resolution IV: Main effects clear; 2FIs aliased with each other
- Resolution V: Main effects and 2FIs clear
Central Composite Design (CCD)
Components:
- $2^k$ factorial points
- $2k$ axial (star) points at distance $\alpha$
- $n_0$ center points
Rotatability Condition:
$$
\alpha = (2^k)^{1/4}
$$
D-Optimal Design
Maximizes the determinant of the information matrix:
$$
\max_{\mathbf{X}} |\mathbf{X}^\top\mathbf{X}|
$$
Equivalently, minimizes the generalized variance of $\hat{\boldsymbol{\beta}}$.
I-Optimal Design
Minimizes average prediction variance:
$$
\min_{\mathbf{X}} \int_{\mathcal{R}} \text{Var}(\hat{y}(\mathbf{x})) \, d\mathbf{x}
$$
Reliability Analysis
Cox Proportional Hazards Model
Hazard Function:
$$
h(t|\mathbf{x}) = h_0(t) \cdot \exp(\mathbf{x}^\top\boldsymbol{\beta})
$$
Where:
- $h(t|\mathbf{x})$ — hazard at time $t$ given covariates $\mathbf{x}$
- $h_0(t)$ — baseline hazard
- $\boldsymbol{\beta}$ — regression coefficients
Partial Likelihood
$$
L(\boldsymbol{\beta}) = \prod_{i: \delta_i = 1} \frac{\exp(\mathbf{x}_i^\top\boldsymbol{\beta})}{\sum_{j \in \mathcal{R}(t_i)} \exp(\mathbf{x}_j^\top\boldsymbol{\beta})}
$$
Where $\mathcal{R}(t_i)$ is the risk set at time $t_i$.
Challenge-Method Mapping
| Manufacturing Challenge | Mathematical Approach |
|------------------------|----------------------|
| High dimensionality | PLS, LASSO, Elastic Net |
| Multicollinearity | Ridge regression, PCR, VIF analysis |
| Spatial wafer patterns | Zernike polynomials, GP regression |
| Hierarchical data | Mixed effects models, REML |
| Nonlinear processes | RSM, polynomial models, transformations |
| Physics constraints | Arrhenius, Preston equation integration |
| Uncertainty quantification | Bayesian methods, bootstrap, prediction intervals |
| Binary outcomes | Logistic regression |
| Count data | Poisson regression |
| Real-time control | Kalman filter, EWMA |
| Time-to-failure | Cox proportional hazards |
Equations Quick Reference
Estimation
$$
\hat{\boldsymbol{\beta}}_{\text{OLS}} = (\mathbf{X}^\top\mathbf{X})^{-1}\mathbf{X}^\top\mathbf{y}
$$
$$
\hat{\boldsymbol{\beta}}_{\text{Ridge}} = (\mathbf{X}^\top\mathbf{X} + \lambda\mathbf{I})^{-1}\mathbf{X}^\top\mathbf{y}
$$
Prediction Interval
$$
\hat{y}_0 \pm t_{\alpha/2, n-k-1} \cdot \sqrt{\text{MSE}\left(1 + \mathbf{x}_0^\top(\mathbf{X}^\top\mathbf{X})^{-1}\mathbf{x}_0\right)}
$$
Confidence Interval for $\beta_j$
$$
\hat{\beta}_j \pm t_{\alpha/2, n-k-1} \cdot \text{SE}(\hat{\beta}_j)
$$
Process Capability
$$
C_p = \frac{\text{USL} - \text{LSL}}{6\sigma}
$$
$$
C_{pk} = \min\left(\frac{\text{USL} - \mu}{3\sigma}, \frac{\mu - \text{LSL}}{3\sigma}\right)
$$
Reference
| Symbol | Description |
|--------|-------------|
| $\mathbf{y}$ | Response vector |
| $\mathbf{X}$ | Design matrix |
| $\boldsymbol{\beta}$ | Coefficient vector |
| $\hat{\boldsymbol{\beta}}$ | Estimated coefficients |
| $\boldsymbol{\varepsilon}$ | Error vector |
| $\sigma^2$ | Error variance |
| $\lambda$ | Regularization parameter |
| $\mathbf{I}$ | Identity matrix |
| $\|\cdot\|_1$ | L1 norm (sum of absolute values) |
| $\|\cdot\|_2$ | L2 norm (Euclidean) |
| $\mathbf{A}^\top$ | Matrix transpose |
| $\mathbf{A}^{-1}$ | Matrix inverse |
| $|\mathbf{A}|$ | Matrix determinant |
| $N(\mu, \sigma^2)$ | Normal distribution |
| $\mathcal{GP}$ | Gaussian Process |
regression test,eval suite,ci
Automated eval suites catch regressions. Run on every model update. Track metrics over time.
regression-based ocd, metrology
Fit model parameters directly.
regression,continuous,predict
Regression predicts continuous values. Linear, polynomial.
regret minimization,machine learning
Goal in online learning to minimize cumulative mistakes.
regularization,dropout,weight decay
Regularization prevents overfitting. Dropout randomly zeros neurons. Weight decay penalizes large weights. Both improve generalization.
rehearsal methods,continual learning
Store and replay old examples.
reinforcement graph gen, graph neural networks
Reinforcement learning guides graph generation through rewards for desired properties like drug-likeness.
reinforcement learning for nas, neural architecture
Use RL to search architectures.
reinforcement learning for scheduling, digital manufacturing
Optimize wafer scheduling with RL.
reinforcement learning,rl,reward
RL learns from rewards by trial and error. Used in game AI, robotics, and RLHF for LLM alignment.
reinforcement learning,rl,reward
RL learns from reward signal. Policy gradient, Q-learning. Games, robotics, RLHF.
reject option,ai safety
Allow model to say "I don't know".
relation extraction as pre-training, nlp
Use relation extraction as pretext task.
relation extraction,knowledge,triple
Relation extraction finds relationships between entities. Build knowledge graphs. (subject, relation, object).
relation extraction,nlp
Identify relationships between entities.
relation networks, neural architecture
Networks explicitly modeling pairwise relations.
relation-aware aggregation, graph neural networks
Relation-aware aggregation weights messages by edge types when updating node representations.
relational knowledge distillation, rkd, model compression
Transfer relationships between samples.
relational reasoning, reasoning
Reason about relationships between entities.
relations diagram, quality & reliability
Relations diagrams map cause-effect relationships for complex problems.