The framework distinguishes seven semiconductor families: Group IV, SiC polytypes, III-V, II-VI, oxides, narrow-gap, and wide-gap, while maintaining concordance across all 23 materials. The clustering is empirically robust: closely related materials, the three SiC polytypes for example, sit on neighboring lattice positions while the wider semiconductor families separate cleanly.
Materials Platform
Materials & Semiconductors
HCE applies a single analytic targeting framework across metals, carbon processing, refractory compounds, and semiconductor fabrication. The platform converts measured transition behavior into deployable processing windows, composition guidance, and fabrication-class deployment plans.
44 / 44
materials concordance
46 / 46
semiconductor dual-property concordance
23 / 23
thermal transfer across domains
20
exact on-rail matches
3
deployment precision classes
The validation package combines CIP-6 materials data and CIP-8 semiconductor measurements into one deployment-oriented view: targets, transfer behavior, and fabrication precision class.
01 — Validated
One framework, validated across
diverse material families.
Across 44 reference phase transitions in metals, carbon processing, and refractory compounds, HCE’s framework predicts measured values with mean absolute agreement of 0.077% and worst-case error of 0.18%. Every reference value lands inside the framework’s deployment tolerance.
Predicted vs measured for 90 reference values across four orders of magnitude in temperature. Every point falls within 0.18% of the framework target.
Composition of the validation footprint. The framework was tested across the full breadth of industrial material classes, not concentrated in any single niche.
Of 90 reference values, 20 fall exactly on lattice targets and a further 52 sit inside the framework's tightest precision class. All 90 fall inside the deployment precision class.
02 — Transferable
The same framework targets
thermal and electronic behavior.
The correction-set framework derived for metals, carbon, and refractories transfers to semiconductor processing temperatures at full concordance, with a single domain-specific extension completing dual-property, thermal plus electronic, coverage across 23 semiconductor materials and 46 paired measurements.
The base correction-set framework transfers from materials to semiconductors at full thermal concordance and requires only one domain-specific addition to complete dual-property coverage.
Match-quality by material and property. Every cell falls inside the deployment precision class; on-rail and near-exact matches dominate.
Family-level clustering across the semiconductor set
03 — Deployable
Three precision classes for real fabrication.
The framework supports deployment at three distinct precision classes, matched to real fabrication contexts, from bulk processing where temperature tolerance is wide, to quantum-confined structures where every degree matters.
Class A — widest tolerance
Bulk processing
Crystal growth, casting, sintering, large-volume carbonization. Processing windows wide enough that material-class targets suffice.
Class B — tighter tolerance
Epitaxial growth
MBE, MOCVD, CVD. Layer-by-layer control where composition-tuned targets and dwell-temperature precision determine defect density and interface quality.
Class C — tightest tolerance
Quantum-confined
Quantum dot synthesis, single-photon emitter fabrication, nanostructured semiconductors. Sub-percent processing precision where the framework's tightest precision class earns its keep.
Deployment workflow
Measure baseline behavior
→
Compute lattice-aligned target
→
Choose processing window
→
Verify against the framework
→
Store specification
Fabrication insertion points
The framework applies at any control lever where a process parameter sets material behavior:
| Control lever | Example deployment | Equipment classes |
|---|---|---|
| Temperature | Carbonization profile, austenization point, growth dwell | Furnaces, RTP, induction heaters |
| Composition ratio | Refractory carbide tuning, alloy precursor mix | Precursor blending, source-flux control |
| Growth rate | Epi layer thickness vs. defect density tradeoffs | MBE, MOCVD, CVD, ALD |
| Pressure / strain | Phase-stability targeting under load | Hot isostatic press, high-pressure cells |
| Substrate gradient | Polytype stability across temperature ramps | Czochralski, float-zone, Bridgman |
| Post-growth anneal | Defect annealing windows, band-gap tuning | RTA, vacuum anneal, oxidation furnaces |
What this is not
HCE’s framework is not a fitted model, not a machine learning approach trained on materials data, and not a CALPHAD-style equilibrium database. It is a first-principles computational method that predicts processing parameters from a small set of fundamental geometric constants. Validation across 44 metals and refractories and 46 semiconductor measurements was conducted under blinded review with cryptographic chain-of-custody.
Independent validation
Each of HCE’s materials, semiconductor, and pharmaceutical filings has been independently spec-blind validated by a PhD-level external reviewer under NDA, with SHA-256 chain-of-custody preserved on datasets and protocols. The protocol structure, datasets and scripts delivered, hashed results submitted, framework revealed only after the reviewer’s outputs are locked, is designed to make the validation auditable rather than rhetorical.
04 · Portfolio
Patent portfolio scope.
CIP-5
Pharma
Pharmaceutical compound classification and therapeutic-index targeting.
CIP-6
Materials
Lattice-guided materials processing: metals, carbon, refractory carbides.
CIP-8
Semiconductors
Semiconductor dual-property prediction across 23 material families.
CIP-10 · filing pending
Extension methods
Adaptive correction-set methods for stability, classification, and extension.
05 · Engage
Working on a materials or semiconductor problem?
HCE engages with industrial partners under NDA to evaluate the platform for specific fabrication challenges. We also work with investors and collaborators interested in the broader portfolio.