Forward and Inverse Virtual Metrology for Phototransistor Gain: A Hierarchical, Uncertainty-Aware Approach for Small Production Datasets

• URL: http://arxiv.org/abs/2608.11868v1
• Date: Wed, 12 Aug 2026 09:51:15 GMT
• Title: Forward and Inverse Virtual Metrology for Phototransistor Gain: A Hierarchical, Uncertainty-Aware Approach for Small Production Datasets
• Abstract summary: We study a problem on a real fabrication history, thirteen to fourteen process runs of a single device.<n>Decomposing the variance of device gain, we find that roughly half of it lies between process runs rather than within them.<n>We provide a forward gain predictor with a relative, uncertainty-aware signal, an inverse search that returns recipes for a target gain.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The customization, optimization and stabilization of the process flow of a silicon bipolar phototransistor commits months of cleanroom time before a finished device can be measured, so a model that predicts device gain from process parameters before a run has value out of proportion to its accuracy. We study this problem on a real fabrication history, thirteen to fourteen process runs of a single device: a small-sample, hierarchically structured setting unlike the large-corpus regime of conventional virtual metrology. Decomposing the variance of device gain, we find that roughly half of it lies between process runs rather than within them, so recipe-only prediction is bounded by construction. Building on these findings we provide a forward gain predictor with a relative, uncertainty-aware signal, an inverse search that returns recipes for a target gain, and, as the foundation for all of it, a multi-level data-quality assessment tailored to the nested physical entities of fabrication (batch, wafer, die) with an explicit cross-level linkage score. The normalized dataset and analysis code are released for full reproducibility.

Related papers

• From Atoms to Entropy: Optimal Noise Allocation for Diffusion Training in the Convex Regime [52.459513910242485]
  We develop a general statistical framework for studying optimal noise-level allocation in diffusion training.<n>We show that the optimized training schedule admits an atomic minimizer, concentrated on finitely many noise levels.<n>We then evaluate the entropic schedule in larger-scale experiments, where full schedule optimization is currently intractable.
  arXiv  Detail & Related papers  (2026-07-10T20:59:06Z)
• PedestrianDiffusion: Multimodal Generative Denoising and Dense State Estimation for Inertial Navigation [1.8620637029128544]
  PedestrianDiffusion is a generative framework that reformulates dense 6D state estimation as a continuous conditional denoising process.<n>It achieves state-of-the-art performance, exhibiting unprecedented robustness to impulse perturbations and coupled 6D kinematic drift.<n>This work provides a rigorous algorithmic blueprint for next-generation Neural Inertial Measurement Units (N-IMUs)
  arXiv  Detail & Related papers  (2026-07-03T14:04:30Z)
• Rethinking Forward Processes for Score-Based Data Assimilation in High Dimensions [2.3550058325130974]
  We propose a measurement-aware score-based filter (MASF) that defines a measurement-aware forward process directly from the measurement equation.<n>For linear measurements, we derive the exact likelihood score and combine it with a learned prior score to obtain the posterior score.<n> Numerical experiments covering a range of settings, including high-dimensional datasets, demonstrate improved accuracy and stability over existing score-based filters.
  arXiv  Detail & Related papers  (2026-04-03T08:55:38Z)
• Sharp Convergence Rates for Masked Diffusion Models [53.117058231393834]
  We develop a total-variation based analysis for the Euler method that overcomes limitations.<n>Our results relax assumptions on score estimation, improve parameter dependencies, and establish convergence guarantees.<n>Overall, our analysis introduces a direct TV-based error decomposition along the CTMC trajectory and a decoupling-based path-wise analysis for FHS.
  arXiv  Detail & Related papers  (2026-02-26T00:47:51Z)
• Trajectory Consistency for One-Step Generation on Euler Mean Flows [24.038760671907024]
  We propose emphEuler Mean Flows (EMF), a flow-based generative framework for one-step and few-step generation.<n>EMF enforces long-range trajectory consistency with minimal sampling cost.
  arXiv  Detail & Related papers  (2026-01-31T04:32:32Z)
• Interpretable Multivariate Conformal Prediction with Fast Transductive Standardization [11.505405148959618]
  We propose a conformal prediction method for constructing tight simultaneous prediction intervals for multiple, potentially related, numerical outputs.<n>This method can be combined with any multi-target regression model and guarantees finite-sample coverage.<n>The core idea is a novel emphcoordinate-wise standardization procedure that makes residuals across output dimensions directly comparable.
  arXiv  Detail & Related papers  (2025-12-17T12:36:00Z)
• Measurement-Guided Consistency Model Sampling for Inverse Problems [2.217547045999963]
  Consistency models enable high-quality generation in a single or only a few steps.<n>We present a modified consistency sampling approach tailored for inverse problem reconstruction.
  arXiv  Detail & Related papers  (2025-10-02T16:53:07Z)
• Distributional Diffusion Models with Scoring Rules [83.38210785728994]
  Diffusion models generate high-quality synthetic data.<n> generating high-quality outputs requires many discretization steps.<n>We propose to accomplish sample generation by learning the posterior em distribution of clean data samples.
  arXiv  Detail & Related papers  (2025-02-04T16:59:03Z)
• Self-Supervised Dataset Distillation for Transfer Learning [77.4714995131992]
  We propose a novel problem of distilling an unlabeled dataset into a set of small synthetic samples for efficient self-supervised learning (SSL) We first prove that a gradient of synthetic samples with respect to a SSL objective in naive bilevel optimization is textitbiased due to randomness originating from data augmentations or masking. We empirically validate the effectiveness of our method on various applications involving transfer learning.
  arXiv  Detail & Related papers  (2023-10-10T10:48:52Z)
• Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction [82.72686460985297]
  We tackle the problem of estimating a Manhattan frame. We derive two new 2-line solvers, one of which does not suffer from singularities affecting existing solvers. We also design a new non-minimal method, running on an arbitrary number of lines, to boost the performance in local optimization.
  arXiv  Detail & Related papers  (2023-08-21T13:03:25Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.