Monte Carlo was invented in 1949. Your 2nm chip deserves better.
QUINSIM replaces brute-force sampling with physics-informed uncertainty quantification that is 1000× faster, captures correlated non-Gaussian process variation, and automatically tells you exactly what is killing your yield.
At sub-5nm nodes, process variation is the dominant design risk. Every standard tool in your stack is fighting it with 75-year-old statistics.
Getting 5σ confidence on an SRAM cell requires 10⁷ SPICE simulations. At 3 seconds each, that's 347 days of compute — per circuit, per corner.
FinFET and GAA threshold voltage distributions are non-Gaussian by physics. Vth in SRAM transistors follows a skewed distribution from line-edge roughness. Every σ value in your PDK is a lie.
PVT corners assume parameters vary independently. In reality, Tox and Vth are nonlinearly coupled. Your worst-case corner is not the circuit's actual failure mode.
Conventional tools identify that yield is low. They cannot tell you which of 57 process parameters is responsible, or by how much. QUINSIM can — analytically, from a single run.
QUINSIM wraps your existing simulator — Spectre, HSPICE, Eldo, Sentaurus — as a black box. No code changes. No porting. Full statistical intelligence in three steps.
QUINSIM ingests foundry measurement data and fits a copula-based joint probability model to your process parameters — capturing non-Gaussian marginals and nonlinear correlations that Gaussian PDK models systematically miss.
Vine Copula · KDE · Rosenblatt TransformFrom 100–500 adaptive SPICE simulations, QUINSIM builds a mathematical surrogate of your circuit that can be evaluated in microseconds. It achieves the statistical accuracy of millions of Monte Carlo runs at a fraction of the cost.
Sparse PCE · Active Learning · LASSOSobol sensitivity indices — computed analytically from PCE coefficients, zero additional cost — rank every parameter by its contribution to yield loss. QUINSIM then auto-centers your design toward maximum yield.
Sobol Indices · EGO · Yield SurfaceStatic Noise Margin (SNM) and write-margin failures at 5σ are the #1 SRAM signoff bottleneck. Cadence Spectre FMC can accelerate, but it is still sampling-based — and still assumes Gaussian distributions that physics violates.
QUINSIM constructs a 57-parameter sparse PCE surrogate from 350 adaptive simulations. The trained surrogate delivers the full 5σ yield surface — probability of failure at every operating point, not just a pass/fail count.
FDSOI processes use intentional back-gate biasing for threshold voltage tuning, creating a strong nonlinear coupling between Vth and device operating point that a Gaussian PDK completely fails to model.
QUINSIM employs a Student-t copula for the Vth–DVT0W pair, capturing heavy-tail co-dependence. The result: accurate prediction of a bimodal NF distribution — a capability no conventional UQ tool can achieve.
Effective Number of Bits (ENOB) degradation from capacitor mismatch, comparator offset, and timing jitter is notoriously difficult to characterize — too many correlated parameters for Monte Carlo to explore efficiently.
QUINSIM's high-dimentional structured PCE handles 150-parameter spaces with 600 adaptive samples, delivering full ENOB probability distribution and failure probability per specification.
Automotive-grade ICs operating from -40°C to 175°C require exhaustive PVT characterization for AEC-Q100 compliance. The combined process × temperature space makes conventional Monte Carlo economically prohibitive.
QUINSIM extends the parameter space to include temperature as an uncertain dimension, building joint surrogates that deliver automotive-grade qualification in hours, not months.
TCAD calibration for a new technology node currently takes 2–4 weeks of manual, deterministic iteration. Uncertainty on calibrated model parameters is unknown. Propagation of that uncertainty to circuit SPICE is entirely absent.
QUINSIM's Bayesian calibration engine uses active learning to select TCAD simulation points, fits a posterior distribution over physical model parameters, and propagates uncertainty through the compact model extraction chain into the UQ-PDK.
Stochastic spectral method that builds a polynomial surrogate from an adaptive, minimal sample set. LASSO regularization identifies the active basis terms, exploiting physical sparsity in circuit responses.
Non-Gaussian marginal CDFs combined with R-vine copula dependence models, mapped to independent uniform space via the Rosenblatt transformation — enabling valid PCE construction for real foundry data.
First-order and total-effect Sobol sensitivity indices computed analytically from PCE coefficients — zero additional simulation cost. Pinpoints which parameters kill yield and by exactly how much.
Kriging-based surrogate optimization using Expected Improvement acquisition functions. Finds yield-maximizing design parameters in fewer than 200 evaluations for 50+ dimensional design spaces.
Whether you are an analog design team, memory IP vendor, foundry, or investor — we want to hear from you.