Pith. sign in

REVIEW 4 major objections 6 minor 33 references

Adaptive Sequential Test Planning for Multi-Mechanism Reliability Qualification via Bayesian Monte Carlo Tree Search

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Sequential Bayesian planning can synthesize damage-aware stress policies that significantly outperform non-adaptive strategies for multi-mechanism reliability qualification under competing failure modes.

desk verdict A serious, well-organized first application of MCTS to adaptive reliability testing, but the headline yield gain is an in-sample artifact of a single-device search and needs a deployed-policy, multi-instance evaluation. read the letter →

arxiv 2608.09622 v1 pith:XVSJOWRV submitted 2026-08-10 cs.AI cs.SYeess.SY

Youssef A. Elhagrasy , Ian Hill , André Ivanov This is my paper · ORCID
classification cs.AIcs.SYeess.SY
keywords reliabilityqualificationadaptivetestplanningMonteCarlotreesearchextendedKalmanfiltercompetingfailuremodesbiastemperatureinstabilityelectromigrationtime-dependentdielectricbreakdown
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Reliability qualification of advanced semiconductor devices currently uses fixed stress recipes that ignore per-device variability and competing failure mechanisms. This paper tries to establish that closed-loop sequential planning can do better: it frames qualification as a partially observable decision problem and solves it by combining Monte Carlo tree search with an extended Kalman filter that tracks latent degradation parameters. In a physics-based simulation of concurrent BTI, electromigration, and time-dependent dielectric breakdown, the adaptive planner's characterization yield rises from about 20% in the first 500 planning iterations to over 54% in the final 500, reaching a 39% cumulative success rate while keeping the best successful sequence well inside safety margins. The claim is that these gains come from learned, damage-aware stress policies rather than from random exploration, and that the approach outperforms non-adaptive baselines.

What carries the argument

The load-bearing mechanism is Monte Carlo tree search for seed-action simulators (MCTS-SA) coupled with extended Kalman filter (EKF) belief estimation. Each decision is a pseudorandom seed that maps deterministically to a stress action $(V,J,T,\Delta t)$, so stochastic simulations can be replayed exactly and planning statistics stay consistent. The EKF maintains a posterior over the latent BTI parameters $(\Delta V_{\max},\tau,\beta)$ and supplies the trace of its covariance as an uncertainty signal. A shaped reward combines progress toward the characterization threshold, an early-progress bonus, a cubic safety barrier that penalizes damage above $D_{\mathrm{thr}}=0.25$, per-epoch damage and uncertainty penalties, and a terminal reward that weights successful characterization at 20,000 against catastrophic failure at 2,000. The interaction of progressive widening with the seed-action space keeps the tree size governed by visit counts rather than by the raw action-grid cardinality, which is what makes finer stress grids nearly free.

What would settle it

Run the same planner on the same device population with the damage indices corrupted by realistic measurement noise or replaced by estimates from a sensor model: if the characterization-yield advantage over a fixed-stress plan shrinks to zero, or if the learned sequences no longer keep $D_{\mathrm{EM}}$ and $D_{\mathrm{TDDB}}$ below 1, the central claim that adaptive planning significantly outperforms non-adaptive strategies in the intended setting would be refuted.

Watch

Extended reading notes

Core claim

The paper's central claim is that multi-mechanism reliability qualification can be treated as a constrained sequential decision problem and solved with tree-search planning, even though degradation is irreversible and the latent device parameters are unknown. The planner chooses stress conditions from a discrete grid, observes noisy threshold-voltage shifts, and updates a Gaussian belief over BTI parameters with an EKF; EM and TDDB damage indices act as directly observable safety constraints. Across 5,000 planning iterations the characterization yield improves monotonically by window, from 20.4% to 54.0%, with cumulative yield 39.2% and a final-window mean return that turns positive. The best successful sequence terminates at epoch 119 with $\Delta V_t \ge 0.09$ V, posterior uncertainty $U=0.044$, and damage fractions $D_{\mathrm{EM}}=0.564$ and $D_{\mathrm{TDDB}}=0.537$, which the paper reads as evidence that the search learns to thread the narrow safe corridor between characterization and catastrophe.

Load-bearing premise

The planner assumes it sees the true EM and TDDB damage indices directly at every decision step, although in real devices those quantities are latent and can only be estimated from indirect electrical signals; the paper calls this perfect-observability version a performance ceiling.

Editorial extensions

If this is right

  • Static qualification recipes derived from population averages can be replaced by per-unit adaptive policies that condition each stress decision on the measurements already taken.
  • The learned strategy of moderating stress as damage approaches the barrier, and preferring temperature over voltage when damage is high, transfers as a design principle for test protocols.
  • Because the planner is anytime, a usable test plan is available well before full convergence, letting the planning budget be matched to available test time.
  • Finer discretization of the stress grid improves achievable yield without combinatorial growth in search-tree size, because progressive widening bounds children by visit count.
  • Ablation shows the safety barrier and early-progress shaping are load-bearing: removing the barrier raises the catastrophe rate to 78% and removing early shaping drops yield to 31%.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: a realistic deployment would need to estimate the damage indices from sensor signals rather than assume perfect observability, likely eroding some of the demonstrated yield gain until the estimator is added.
  • Beyond the paper: the same formulation should transfer to battery aging or structural fatigue, where multiple competing wear-out mechanisms constrain accelerated testing.
  • Beyond the paper: a population-level policy that marginalizes over device parameters, plus distillation of the tree search into a fast approximator, would convert instance-optimal sequences into reusable real-time test recipes.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper formulates reliability qualification of advanced semiconductor devices as a partially observable sequential decision problem in which a planner chooses stress conditions (voltage, current density, temperature, duration) to maximize the chance of characterizing BTI degradation before EM or TDDB damage becomes catastrophic. The proposed framework combines Monte Carlo tree search with a seed-action simulator (MCTS-SA) and an extended Kalman filter for online Bayesian inference over latent BTI parameters. The objective is a multi-component reward with terminal success/catastrophe terms, safety barriers, and shaping terms. The main empirical claim is that, across a single 5,000-iteration planning run on one simulated device, characterization yield rises from about 20% in the first 500 iterations to about 54% in the final 500, with 39.2% cumulative yield, and that this outperforms random and fixed-stress baselines. The paper also reports ablation studies, robustness sweeps, and an explicit limitation section.

Significance. The problem is relevant and the proposed formulation is a plausible extension of tree-search planning to reliability qualification. A strength of the manuscript is its candor: the direct-observability assumption is labeled a performance ceiling in Section VIII-E, the simulation-only nature of validation is stated, and the reward weights are acknowledged as calibration choices. If the reported improvement were reproduced under a properly held-out evaluation protocol and across multiple device instances, the work would be a useful contribution to adaptive test planning. However, the current evidence is in-sample, single-instance, and conditioned on ideal observability, so the significance claimed in the abstract is not yet established.

major comments (4)
  1. [§VII-D1, Table V, Figure 1] The headline comparison is in-sample. Characterization yield is measured on the MCTS search's own rollouts, and by the final 500-iteration window the tree has essentially converged: root entropy is 0.055 and the most-visited root action receives 97% of visits (Section VIII-D). The final-window CY is therefore dominated by repeated exploitation of a few discovered high-return branches for the same device instance, not by independent draws from a deployable adaptive policy. The random and fixed-stress baselines, in contrast, are evaluated from scratch with no search budget. The comparison conflates 'the search found a good path for this one device' with 'the planner outperforms non-adaptive strategies.' To support the central claim, the authors should freeze a policy from the search (e.g., the best root action or a plan derived from the converged tree) and evaluate it on held-out rollouts, ideally on fresh device instances.
  2. [§VII-D1, Table V; §VII-E] All quantitative results are obtained on a single device instance: Table V explicitly states 'same device instance,' and Algorithm 1 resets the environment with the same master seed each iteration. The abstract's claim that the planner 'significantly outperform[s] non-adaptive strategies for reliability qualification' is population-level in wording, but the evidence is instance-conditional. Section VII-E acknowledges the distinction and defers the population-level formulation (29) to future work, but the main conclusion is not correspondingly qualified. Reporting results over multiple sampled device instances, or at least per-instance box plots, is necessary before the stated conclusion can be drawn.
  3. [§III-C, §VIII-E, Eq. (19)] The planner's safety mechanism relies on exact knowledge of D_EM and D_TDDB: the barrier penalty (Eq. 19), the damage-rate penalty, and catastrophe termination all require these values as inputs. Section VIII-E correctly states that in physical devices these indices are latent and must be estimated, and that the perfect-observability version is a performance ceiling. This is an honest limitation, but it is also load-bearing: the reported 54% final-window yield is obtained under an idealization, and the paper does not quantify how the result degrades when damage is observed through noisy indirect signals. Since the stated contribution is to reliability qualification practice, the main quantitative comparison should be complemented by a sensitivity analysis under noisy or estimated damage, or the results should be presented solely as an upper-bound benchmark.
  4. [§V, Eqs. (16)–(23), Eq. (27)] The paper claims, following Ng et al., that the multi-component objective provides intermediate feedback 'without altering which test sequences are ultimately optimal.' Ng et al.'s policy-invariance theorem applies to potential-based shaping rewards of the form F = γΦ(s') − Φ(s); the additive terms r_prog, r_soft, r_prox, r_ΔD, r_U, and r_stall in Eqs. (17)–(22) are not shown to be representable as potential differences, and they accumulate along the trajectory, so they can change the total return ordering of sequences. Since the 'best successful sequence' is selected by maximizing shaped return in Eq. (27), the claim that this sequence is optimal under the terminal objective is not justified. The authors should either prove the potential-based property for their shaping terms or revise the claim to state that the components are heuristic shaping terms that trade off multiple objectives.
minor comments (6)
  1. [Abstract, §VII-D1, Table V] The first-500 and final-500 yields are reported inconsistently: the abstract and Figure 1 say 20.4% and 54.2%, while Table V says 19.4% and 54.0%. Please harmonize the numbers.
  2. [§VII-D1 vs. §VIII-D] Root entropy diagnostics are inconsistent: Section VII-D1 says entropy decreases from 0.77 at iteration 100 to 0.06 by iteration 500, while Section VIII-D says it decreases from 1.0 at iteration 10 to 0.055 at iteration 5,000. Please unify the description of the convergence diagnostics.
  3. [§III-D, Eq. (6)] In Eq. (6), the symbol D is used for the action set of durations Δt, but D is also used throughout for damage indices D_EM and D_TDDB. Use Δt or another symbol for the action set to avoid ambiguity.
  4. [Table I] The note that σ_T = σ_A = sqrt(ln(1 + 0.082)) is unclear as printed; it should read sqrt(ln(1 + 0.08²)) or the intended value should be stated explicitly.
  5. [§VII-D10] The ablation statement that removing the safety barrier 'increases catastrophe rate to 78%' needs an explicit baseline comparison: 78% is close to the first-500-window rate (79.8%) and to the fixed-stress baseline (78%), and far above the final-window rate (46%). Clarify the comparison point for the ablation.
  6. [Figure 1] The x-axis labels list '500' multiple times (0 500 500 1000 ...), which obscures the window boundaries. Please simplify the axis to show window indices or endpoint values.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the yield improvement is an emergent MCTS search outcome on a fully specified simulator; self-citations are non-load-bearing.

full rationale

The paper's derivation chain is explicit: it defines the POMDP and qualification outcomes (Section III), gives the full degradation equations and parameter distributions (Section IV, Eqs. 7-11, Tables I-II), specifies the reward (Section V, Table III), runs MCTS-SA (Section VI), and then measures characterization yield on rollouts (Section VII-D). The reported CY values are outcome statistics of the search, not quantities defined in terms of the reward weights or fitted to earlier data. The reward constants are openly labeled as calibration/tuning values ('These constants were selected by calibration on the simulated environment to preserve the relative ordering of promising versus unsafe trajectories. They are tuning values for this study rather than universal constants.'), and no equation reduces the final yield to those constants. The use of the authors' own Gerabaldi simulator [15] is a self-citation, but the paper states all relevant degradation laws and parameter choices inline, so the environment is independently specified and the citation is not load-bearing. The direct observability of D_EM and D_TDDB is an acknowledged idealization, explicitly called a performance ceiling in Section VIII-E, not a hidden circular input. The in-sample comparison (MCTS last 500 rollouts vs. random/fixed baselines from scratch on the same device) and the larger search budget for the MCTS best-return are threats to external validity and benchmark fairness, but they are not circularity: they do not make the claimed improvement equivalent to the paper's own definitions, fitted parameters, or self-citations. Overall, the central claim is an emergent empirical result of search on a fully specified simulator, so no significant circularity is present.

Assumptions & free parameters 11 free parameters · 6 assumptions · 0 invented entities

The central results rest on a heavily parameterized simulation environment and a hand-tuned reward. The mechanism parameters (Table II) are calibration choices to create a competitive regime; the reward weights (Table III) are tuned on the same simulator. Two explicit simplifications, Gaussian noise and direct observability of competing damage, are acknowledged. No new physical entities are introduced.

free parameters (11)
  • A_EM (EM rate prefactor) = 1.97e-7
    Calibrated so EM failure timescale matches BTI and TDDB at reference stress (Section IV-C, Table II).
  • A_TDDB (TDDB rate prefactor) = 1.53e-6
    Calibrated to match TDDB timescale to the other mechanisms (Table II).
  • Q (EM activation energy) = 0.80 eV
    Chosen as representative industrial value; no fitting shown.
  • Ea (TDDB activation energy) = 0.70 eV
    Chosen as representative industrial value.
  • n (EM current exponent) = 2.0
    Standard Black's equation exponent, chosen.
  • gamma (TDDB voltage exponent) = 3.0
    Chosen E-model exponent.
  • BTI mean tau (time constant) = 19840 hr (Table II); Table I lists 28000 hr (inconsistent)
    Calibrated so BTI failure occurs around 150 epochs at reference stress; used in the simulator, while the EKF prior and Table I use 28000.
  • BTI mean DeltaV_max = 0.20 V
    Sets the characterization threshold (0.09 V) to ~45% of asymptotic shift; calibrated.
  • Reward weights (Table III: R_fail, R_cat, w_U, w_dp, w_close, w_pow, w_soft, D_thr, w_prox, w_dD, c_live, etc.) = See Table III
    Hand-tuned on the simulator to preserve ordering of promising vs unsafe trajectories; not universal constants (Section V).
  • EKF prior mean and covariance, measurement noise sigma_meas, process noise q = mu0=(0.16, log28000, 0.5), Sigma0=diag(0.04,0.25,0.08), sigma_meas=3e-3, q=5e-7
    Chosen by the authors; the prior mean for tau (28000) differs from the calibrated environment value (19840), which is presented as part of robustness.
  • MCTS hyperparameters (C, k, alpha) = C=1.4, k=3, alpha=0.5
    Standard/default choices for UCB and progressive widening; low sensitivity reported in Table VII.
assumptions (6)
  • domain assumption BTI degradation follows the stretched-exponential model (Eq. 7) with voltage/temperature acceleration (Eq. 8).
    Standard compact BTI model from Grasser et al. [4] and Mahapatra et al. [5]; the EKF measurement model uses this same equation.
  • domain assumption EM damage accumulates via Black's equation (Eq. 10) and TDDB via an E-model (Eq. 11), with damage indices summing stress-duration/MTTF ratios.
    Standard industrial acceleration models [27],[28]; the paper uses them as the simulator's ground truth.
  • domain assumption Per-device latent parameters are sampled from the distributions in Table I and remain fixed during a test.
    Standard practice for population variability [8],[5]; this variability creates the planning challenge.
  • ad hoc to paper Measurement noise is Gaussian with fixed variance (Eq. 12).
    Adopted for EKF closed-form updates; the paper acknowledges that TDDB noise may be skewed or heavy-tailed and lists alternative filters (Section IV-D).
  • ad hoc to paper DEM and DTDDB are directly observable proxies at every epoch.
    Acknowledged simplification (Sections III-C and VIII-E); the planner depends on these for the safety barrier and termination.
  • ad hoc to paper The multi-component reward preserves the optimal test sequence ordering per reward-shaping theory.
    The paper cites Ng et al. [29], but the shaped terms (e.g., w_close*p, w_pow*p^2, r_prox, r_dD) do not appear to be potential-based, so the invariance claim is not demonstrated.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Adaptive Sequential Test Planning for Multi-Mechanism Reliability Qualification via Bayesian Monte Carlo Tree Search." pith.science (2026). https://pith.science/paper/XVSJOWRV

@misc{pith2026260809622,
  author       = {Pith},
  title        = {Pith review of: Adaptive Sequential Test Planning for Multi-Mechanism Reliability Qualification via Bayesian Monte Carlo Tree Search},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XVSJOWRV}},
  note         = {Machine review of arXiv:2608.09622}
}
read the original abstract

Reliability qualification of advanced semiconductor devices requires sequential stress decisions that balance characterization objectives against multiple competing failure mechanisms. Current practice relies on static test plans derived from population-level acceleration models, which cannot adapt to per-unit variability or real-time degradation observations. This paper presents a closed-loop adaptive test planning framework that formulates reliability qualification as a partially observable sequential decision problem and solves it using Monte Carlo tree search for seed-action simulators (MCTS-SA) coupled with extended Kalman filter (EKF) belief-state estimation. The framework models stochastic, per-device variability in bias temperature instability (BTI), electromigration (EM), and time-dependent dielectric breakdown (TDDB), and treats stress selection as a constrained sequential optimization, i.e., to maximize the probability of successful degradation characterization while respecting catastrophic failure constraints. Under the experimental assumptions used here (discrete stress actions, proxy damage observability, and cumulative degradation without recovery), we believe this to be a novel application of tree-search-based adaptive test planning to multi-mechanism reliability qualification. Across 5,000 planning iterations, the characterization yield (CY) improves from 20% in the first 500 iterations to over 54% in the final 500, with 39% cumulative success, while the best successful test sequence terminates with EM and TDDB damage fractions DEM=0.564 and DTDDB=0.537, well within safety margins. These results demonstrate that sequential Bayesian planning can synthesize damage-aware test policies that significantly outperform non-adaptive strategies for reliability qualification under competing failure modes.

Figures

Figures reproduced from arXiv: 2608.09622 by the authors.

Figure 1
Figure 1. shows CY across successive 500-iteration windows, increasing from 20.4% to 54.2%, with 39.2% cumulative yield. This improvement indicates that the planner progressively learns to navigate the stress–damage space more effectively. In this setting, 100% CY is not achievable due to per-device variability and competing mechanism constraints, which place the most adverse instances beyond reliable characterization within … view at source ↗
Figure 2
Figure 2. BTI degradation progress over the best test sequence. The planner [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. EM and TDDB damage accumulation over the best test sequence. Both [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

33 extracted references · 25 canonical work pages

  1. [1]

    Systematical study of 14nm FinFET reliability: From device level stress to product HTOL,

    C. Liu, H. Sagong, H. Kim, S. Choo, H. Lee, Y . Kim, H. Kim, B. Jo, M. Jin, J. Kim, S. Ha, S. Pae, and J. Park, “Systematical study of 14nm FinFET reliability: From device level stress to product HTOL,” inIEEE International Reliability Physics Symposium (IRPS), 2015

    work page 2015

  2. [2]

    Reli- ability studies of a 10nm high-performance and low-power CMOS tech- nology featuring 3rd generation FinFET and 5th generation HK/MG,

    A. Rahman, J. Dacuna, P. Nayak, G. Leatherman, and S. Ramey, “Reli- ability studies of a 10nm high-performance and low-power CMOS tech- nology featuring 3rd generation FinFET and 5th generation HK/MG,” inIEEE International Reliability Physics Symposium (IRPS), 2018, pp. 6F.4.1–6F.4.6

    work page 2018

  3. [3]

    Utilizing a thorough understanding of critical aging and failure mechanisms in FinFET technologies to enable reliable high performance circuits,

    B. Weir, V . Prasad, S. Moinian, S. J. Park, J. Blasko, J. Brown, and J. Pallinti, “Utilizing a thorough understanding of critical aging and failure mechanisms in FinFET technologies to enable reliable high performance circuits,” inIEEE International Reliability Physics Symposium (IRPS), 2019, pp. 1–6

    work page 2019

  4. [4]

    The paradigm shift in understanding the bias temperature instability: From reaction-diffusion to switching oxide traps,

    T. Grasser, B. Kaczer, W. Goes, T. Aichinger, P. Hehenberger, and M. Nelhiebel, “The paradigm shift in understanding the bias temperature instability: From reaction-diffusion to switching oxide traps,”IEEE Transactions on Electron Devices, vol. 58, no. 11, pp. 3652–3666, 2011

    work page 2011

  5. [5]

    A comparative study of different physics-based NBTI models,

    S. Mahapatra, N. Goel, S. Desai, S. Gupta, B. Jose, S. Mukhopadhyay, K. Joshi, A. Jain, A. E. Islam, and M. A. Alam, “A comparative study of different physics-based NBTI models,”IEEE Transactions on Electron Devices, vol. 60, no. 3, pp. 901–916, 2013

    work page 2013

  6. [6]

    JESD47: Stress-test-driven qualification of integrated circuits,

    JEDEC Solid State Technology Association, “JESD47: Stress-test-driven qualification of integrated circuits,” JEDEC Standard, 2016. [Online]. Available: https://www.jedec.org/standards-documents/docs/jesd47

    work page 2016

  7. [7]

    W. B. Nelson,Accelerated Testing: Statistical Models, Test Plans, and Data Analysis. John Wiley & Sons, 2004

    work page 2004

  8. [8]

    W. Q. Meeker and L. A. Escobar,Statistical Methods for Reliability Data. John Wiley & Sons, 1998

    work page 1998

Show all 33 references
  1. [9]

    Bayesian experimental design: A review,

    K. Chaloner and I. Verdinelli, “Bayesian experimental design: A review,” Statistical Science, vol. 10, no. 3, pp. 273–304, 1995

    1995

  2. [10]

    Planning and acting in partially observable stochastic domains,

    L. P. Kaelbling, M. L. Littman, and A. R. Cassandra, “Planning and acting in partially observable stochastic domains,”Artificial Intelligence, vol. 101, no. 1–2, pp. 99–134, 1998

    1998

  3. [11]

    Adaptive stress testing: Finding likely failure events with reinforcement learning,

    R. Lee, O. J. Mengshoel, A. Saksena, R. W. Gardner, D. Genin, J. Silber, M. Ahmed, and M. J. Kochenderfer, “Adaptive stress testing: Finding likely failure events with reinforcement learning,”arXiv preprint arXiv:1811.02188, 2018. [Online]. Available: https://arxiv.org/abs/1811.02188

    2018 arXiv

  4. [12]

    A survey of algorithms for black-box safety validation of cyber-physical systems,

    A. Corso, R. J. Moss, M. Koren, R. Lee, and M. J. Kochenderfer, “A survey of algorithms for black-box safety validation of cyber-physical systems,”Journal of Artificial Intelligence Research, vol. 72, pp. 377– 428, 2021

    2021

  5. [13]

    Thrun, W

    S. Thrun, W. Burgard, and D. Fox,Probabilistic Robotics. MIT Press, 2005

    2005

  6. [14]

    A survey of monte carlo tree search methods,

    C. B. Browne, E. Powley, D. Whitehouse, S. M. Lucas, P. I. Cowling, P. Rohlfshagen, S. Tavener, D. Perez, S. Samothrakis, and S. Colton, “A survey of monte carlo tree search methods,”IEEE Transactions on Computational Intelligence and AI in Games, vol. 4, no. 1, pp. 1–43, 2012

    2012

  7. [15]

    Gerabaldi: A temporal simulator for probabilistic ic degradation and failure processes,

    I. Hill and A. Ivanov, “Gerabaldi: A temporal simulator for probabilistic ic degradation and failure processes,” inIEEE 41st VLSI Test Symposium (VTS), 04 2023, pp. 1–7

    2023

  8. [16]

    Optimal accelerated test framework for time-dependent dielectric breakdown lifetime parameter estimation,

    Y .-D. Wu, K. Yang, S.-H. Hsu, and L. Milor, “Optimal accelerated test framework for time-dependent dielectric breakdown lifetime parameter estimation,”IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 28, no. 12, pp. 2658–2671, 2020

    2020

  9. [17]

    Adaptive stress testing for autonomous vehicles,

    M. Koren, S. Alsaif, R. Lee, and M. J. Kochenderfer, “Adaptive stress testing for autonomous vehicles,” inIEEE Intelligent Vehicles Symposium (IV), 2018, pp. 1–7

    2018

  10. [18]

    Adaptive stress testing with reward augmentation for autonomous vehicle valida- tion,

    A. Corso, P. Du, K. Driggs-Campbell, and M. J. Kochenderfer, “Adaptive stress testing with reward augmentation for autonomous vehicle valida- tion,” inIEEE Intelligent Transportation Systems Conference (ITSC), 2019, pp. 1–7

    2019

  11. [19]

    R. S. Sutton and A. G. Barto,Reinforcement Learning: An Introduction, 2nd ed. MIT Press, 2018

    2018

  12. [21]

    Bandit based monte-carlo planning,

    L. Kocsis and C. Szepesv ´ari, “Bandit based monte-carlo planning,” in European Conference on Machine Learning (ECML), 2006, pp. 282– 293

    2006

  13. [22]

    Efficient selectivity and backup operators in monte-carlo tree search,

    R. Coulom, “Efficient selectivity and backup operators in monte-carlo tree search,” inComputers and Games, 2007, pp. 72–83

    2007

  14. [23]

    Mastering the game of go with deep neural networks and tree search,

    D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V . Panneershelvam, M. Lanc- totet al., “Mastering the game of go with deep neural networks and tree search,”Nature, vol. 529, no. 7587, pp. 484–489, 2016

    2016

  15. [24]

    Model predictive path integral control: From theory to parallel computation,

    G. Williams, A. Aldrich, and E. A. Theodorou, “Model predictive path integral control: From theory to parallel computation,”Journal of Guidance, Control, and Dynamics, vol. 40, no. 2, pp. 344–357, 2017

    2017

  16. [25]

    New extension of the kalman filter to nonlinear systems,

    S. J. Julier and J. K. Uhlmann, “New extension of the kalman filter to nonlinear systems,” inSignal Processing, Sensor Fusion, and Target Recognition VI, vol. 3068. SPIE, 1997, pp. 182–193

    1997

  17. [26]

    A critical examination of the mechanics of dynamic nbti for pmosfets,

    M. A. Alam and S. Mahapatra, “A critical examination of the mechanics of dynamic nbti for pmosfets,” in2003 IEEE International Reliability Physics Symposium Proceedings, 41st Annual.IEEE, 2003, pp. 23–29

    2003

  18. [27]

    Electromigration: A brief survey and some recent results,

    J. R. Black, “Electromigration: A brief survey and some recent results,” IEEE Transactions on Electron Devices, vol. 16, no. 4, pp. 338–347, 1969

    1969

  19. [28]

    Reliability challenges for 45nm and beyond,

    J. W. McPherson, “Reliability challenges for 45nm and beyond,” in Proceedings of the 43rd Design Automation Conference (DAC), 2006, pp. 176–181

    2006

  20. [29]

    Policy invariance under reward transformations: Theory and application to reward shaping,

    A. Y . Ng, D. Harada, and S. Russell, “Policy invariance under reward transformations: Theory and application to reward shaping,” inInterna- tional Conference on Machine Learning (ICML), 1999, pp. 278–287

    1999

  21. [30]

    Finite-time analysis of the multiarmed bandit problem,

    P. Auer, N. Cesa-Bianchi, and P. Fischer, “Finite-time analysis of the multiarmed bandit problem,”Machine Learning, vol. 47, no. 2–3, pp. 235–256, 2002

    2002

  22. [31]

    Progressive strategies for Monte-Carlo tree search,

    G. M. J.-B. Chaslot, M. H. M. Winands, H. J. van den Herik, J. W. H. M. Uiterwijk, and B. Bouzy, “Progressive strategies for Monte-Carlo tree search,”New Mathematics and Natural Computation, vol. 4, no. 3, pp. 343–357, 2008

    2008

  23. [32]

    Gate leakage current integration- based dielectric breakdown monitor in a 12nm FinFET process,

    M. Rend ´on, I. Hill, and A. Ivanov, “Gate leakage current integration- based dielectric breakdown monitor in a 12nm FinFET process,” in2025 IEEE 43rd VLSI Test Symposium (VTS), Tempe, AZ, USA, 2025. IEEE TRANSACTIONS ON RELIABILITY , VOL. XX, NO. X, MONTH YEAR 14 Youssef Elha...

    2025

  24. [2017]

    Available: https://arxiv.org/abs/1707.06347

    [Online]. Available: https://arxiv.org/abs/1707.06347

    arXiv

  25. [2027]

    His research interests include semiconductor reliability, hardware–software co-design for machine learning acceleration, and VLSI system design. He has co-authored another publication in Advanced Materials, and is currently working on other re- search projects, spanning CMOS a...

    2025

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.