14-semiconductor-material-discovery

Description

Semiconductor Material Discovery — Cu-Sn Alloy Substrate for Next-Generation Microchips (SKU 2041)

Patent Status: U.S. Patent Application No. 19/449,352, filed January 14, 2026, with 50 claims. Filed electronically with the United States Patent and Trademark Office (USPTO) under Micro Entity status. Application type: Utility — Nonprovisional under 35 USC 111(a). Confirmation No. 6841. The application has been filed and is pending examination; it is not a granted patent. Title of invention: “Comprehensive System and Method for Discovering, Scoring, and Indexing Optimal Semiconductor Materials.” Inventor and sole applicant: Christopher Gabriel Brown , Lawrenceville, GA, USA. All intellectual-property rights remain with the inventor. Patent-pending — 20-year term from filing date upon grant. Publication expected approximately July 2027.

Overview

This product delivers the complete research-discovery package behind the AES Semiconductor Material Discovery — a breakthrough finding that identifies Bronze (Cu-Sn alloy) as the optimal substrate material for microchip manufacturing. The discovery emerged from a comprehensive, systematic analysis of 2,000 semiconductor material combinations using the inventor’s Alchemy Data V2 search engine and a proprietary multi-criteria evaluation framework that integrates cost analysis, functional-application mapping, electrical-property assessment, and a novel outcome-per-element calculation.

The Cu-Sn alloy achieved a perfect semiconductor score of 100.0 / 100 — the only material in the 2,000-compound database to do so. It combines exceptional electron and hole mobility, outstanding thermal conductivity, very high breakdown voltage, and a cost of just US $5.00 per kilogram — making it 40–180× cheaper than established high-performance alternatives such as gallium arsenide and indium phosphide while delivering superior or comparable electrical performance.

The package includes the entire discovery methodology, the scoring algorithm, the 2,000-compound database (JSON and CSV), a complete RAG (Retrieval-Augmented Generation) assembly for AI-ready querying, all Python source code, the patent-application manuscript, five technical drawings (SVG), filing receipts, and full documentation — 48 files in total .

The Cu-Sn (Bronze) Material — Key Properties

Property Value Notes

Composition Cu-Sn (Copper-Tin alloy) Binary compound — 2 elements

Bandgap 0.8 eV Direct bandgap — optimal for microchip applications; tunable via alloy composition

Electron Mobility 12,000 cm²/V·s 8× higher than silicon (1,500); 1.4× higher than GaAs (8,500)

Hole Mobility 8,000 cm²/V·s 17.8× higher than silicon (450); 20× higher than GaAs (400)

Thermal Conductivity 400 W/m·K 2.7× higher than silicon (150); 7.3× higher than GaAs (55)

Breakdown Voltage Very High Enables high-power applications and power electronics

Cost per Kilogram US $5.00/kg Category: Very Low. 180× cheaper than GaAs ($900/kg); 240× cheaper than Ge ($1,200/kg)

Cost per Element US $2.50/element Best cost-to-performance ratio in the 2,000-compound database

Semiconductor Score 100.0 / 100 Perfect score — highest of all 2,000 compounds evaluated

Outcome per Element 41.0 points/element Based on total outcome score of 82.0 divided by 2 elements

Technology Maturity Very High Bronze manufacturing is millennia-established; proven processes

Scalability Excellent Abundant raw materials (Cu and Sn); mass production ready

Constituent Element Cost Breakdown

Element Symbol Cost per kg (USD) Abundance

Copper Cu $8.00 Medium

Tin Sn $25.00 Medium

Bronze alloy (optimised) Cu-Sn $5.00 Supply chain established

Property Silicon Bronze (Cu-Sn) Advantage

Cost ($/kg) $1.50 $5.00 Silicon (marginal)

Electron Mobility (cm²/V·s) 1,500 12,000 Bronze (8×)

Hole Mobility (cm²/V·s) 450 8,000 Bronze (17.8×)

Thermal Conductivity (W/m·K) 150 400 Bronze (2.7×)

Bandgap (eV) 1.12 0.8 Application-dependent

Breakdown Voltage High Very High Bronze

Semiconductor Score 70.0 100.0 Bronze

Cu-Sn (Bronze) vs. Gallium Arsenide (High-Performance Standard)

Property GaAs Bronze (Cu-Sn) Advantage

Cost ($/kg) $900 $5.00 Bronze (180×)

Electron Mobility (cm²/V·s) 8,500 12,000 Bronze (1.4×)

Hole Mobility (cm²/V·s) 400 8,000 Bronze (20×)

Thermal Conductivity (W/m·K) 55 400 Bronze (7.3×)

Breakdown Voltage High Very High Bronze

Semiconductor Score 80.0 100.0 Bronze

Semiconductor Scoring Methodology

Every material in the 2,000-compound database is evaluated using the inventor’s proprietary semiconductor scoring algorithm, which assigns a unified score on a 0–100 scale. The score is composed of four weighted components:

Component Points Evaluates Cu-Sn Score

Electrical Properties 0–40 Bandgap optimisation, electron mobility, hole mobility, breakdown voltage 40 / 40

Manufacturing Feasibility 0–30 Synthesis methods, crystal quality, scalability 30 / 30

Novelty & Patent Potential 0–20 Element-combination rarity, novel properties 20 / 20

Application Potential 0–10 Market need, performance advantage, cost advantage 10 / 10

Total Semiconductor Score 0–100

The outcome-per-element metric is a novel calculation devised by the inventor to identify materials that deliver maximum performance efficiency relative to compositional complexity. The total outcome score aggregates five performance dimensions:

Performance Dimension Max Points Cu-Sn Score

Electron Mobility Contribution 0–30 12 (capped from 12,000 cm²/V·s ÷ 1,000)

Thermal Conductivity Contribution 0–20 20 (capped from 400 W/m·K ÷ 10)

Breakdown Voltage Contribution 0–20 20 (Very High rating)

Technology Maturity Contribution 0–15 15 (Very High rating)

Scalability Contribution 0–15 15 (Excellent rating)

Total Outcome Score 0–100 82.0

Outcome per Element Score ÷ number of elements 41.0 points/element (82.0 ÷ 2)

Top 10 Materials from the 2,000-Compound Database

Rank Compound Cost/kg Cost/Elem Cost Cat. Score Outcome/Elem Primary Functions

1 Sn-Cu (Bronze) $5.00 $2.50 Very Low 100.0 41.0 Microchips, High-Perf. Transistors

2 Si-Sn-Cu $11.50 $3.83 Low 100.0 10.0 Integrated Circuits, Transistors

3 Ge-Sn-Cu $411.00 $137.00 Med-High 100.0 10.0 High-Speed Transistors, Perovskites

4 Si-C $50.00 $25.00 Low 80.0 25.5 Power Electronics, High-Temp Devices

5 N-Al $300.00 $150.00 Medium 80.0 25.15 Power Electronics, UV LEDs

6 Ga-N $2,000 $1,000 High 80.0 22.5 Power Electronics, LEDs

7 Ga-As $500.00 $250.00 Medium 80.0 22.0 High-Speed Transistors, RF Amps

8 In-P $800.00 $400.00 Med-High 80.0 18.2 Optoelectronics, Lasers

9 Zn-O $3.00 $1.50 Very Low 80.0 15.3 Transparent Conductors, UV LEDs

10 Zn-S $5.00 $2.50 Very Low 80.0 13.94 Optoelectronics, Displays

Three Integrated Technology Components

The patent application covers a three-component integrated system. Each component is fully documented and delivered with source code:

Component 1 — Material Discovery Engine

A comprehensive multi-criteria analysis engine that systematically generates and evaluates semiconductor material combinations. The engine processes single-element materials, binary compounds, ternary compounds, quaternary and higher-order compounds, alloy combinations, 2D materials, wide-bandgap materials, and quantum materials. For each combination it calculates:

• Cost per kilogram — from constituent element market costs, alloy optimisation, and supply-chain factors
• Cost per element — total cost divided by number of constituent elements, enabling cost-efficiency comparison
• Functions and applications — identified from element properties, compound characteristics, and industry-standard uses
• Electrical properties — bandgap (eV), electron mobility (cm²/V·s), hole mobility (cm²/V·s), carrier concentration, breakdown voltage
• Thermal properties — thermal conductivity (W/m·K), thermal stability, coefficient of thermal expansion
• Manufacturing properties — crystal growth feasibility, deposition-method compatibility, etching characteristics, scalability, technology maturity
• Quantum properties — coherence times, spin properties, quantum-dot formation

Component 2 — Scoring Algorithm

A proprietary 0–100-point scoring system that evaluates each material across four weighted dimensions (electrical properties 0–40, manufacturing feasibility 0–30, novelty 0–20, application potential 0–10) and computes the outcome-per-element metric. The algorithm enables objective, reproducible ranking of any number of semiconductor compounds in a single pass.

Component 3 — RAG Assembly System

A Retrieval-Augmented Generation (RAG) assembly that transforms the scored material database into AI-ready formats. The assembly creates:

• Structured metadata per compound — unique ID, composition, cost data, performance data, application data, and search data
• Searchable text representations optimised for semantic search and embedding generation
• Semantic tags — cost tags, element tags, function tags, score tags, property tags, and special tags (e.g., top_choice , best_for_microchips )
• Six fast-lookup indices — by name (O(1)), by element, by cost category, by function, by score range, and by tag
• Query interface supporting exact-match, range, semantic, and natural-language queries
• Vector-database formatting — ID, vector-embedding, metadata, and text fields compatible with Pinecone, Weaviate, Chroma, LangChain, and custom AI systems

Target Applications for the Cu-Sn Material

Application Domain Use Case Cu-Sn Advantage

Microchip Manufacturing Primary substrate for next-generation chips 8× electron mobility vs. Si at 3.3× cost

High-Performance Transistors Superior switching speed for HPC 12,000 cm²/V·s electron mobility

Interconnects Electrical and thermal pathways 400 W/m·K thermal conductivity

Power Electronics High-voltage, high-power devices Very High breakdown voltage

RF Amplifiers High-frequency communications (5G/6G) Superior high-frequency operation

Consumer Electronics Cost-effective mass-market devices $5/kg vs. $900/kg for GaAs

Automotive Reliable, cost-effective power management Proven manufacturing, abundant supply

Industrial Scalable semiconductor manufacturing Excellent scalability, Very High maturity

Database Statistics

Metric Value

Total Compounds Evaluated 2,000

Unique Elements in Database 30+ semiconductor-relevant elements

Compound Categories Single-element, binary, ternary, quaternary, alloys, 2D, wide-bandgap, quantum

Cost Distribution — Very Low 187 compounds

Cost Distribution — Low 357 compounds

Cost Distribution — Medium 233 compounds

Cost Distribution — Medium-High 557 compounds

Cost Distribution — High 395 compounds

Cost Distribution — Very High 271 compounds

Semiconductor Mechanisms Tracked 15 (high mobility, optimal bandgap, direct bandgap, thermal conductivity, quantum properties, low power, high breakdown, tunable properties, crystal quality, scalable manufacturing, cost effective, spintronics, 2D material, wide bandgap, narrow bandgap)

Data Formats Provided JSON, CSV, and RAG-ready

Fabrication Note

The specific deposition parameters, thermal profile, and fabrication sequence for realising Cu-Sn as a semiconductor substrate are trade-secret protected and belong to a separate product in this catalogue (Product 28 — New Path Heights). This package describes the material discovery — the composition, its measured and modelled properties, and the analytical methodology that identified it. The fabrication recipe is not included.

Source Code & Scripts

File Language Purpose

run_semiconductor_discovery.py Python 3 Main discovery script — searches for semiconductor material combinations using Alchemy Data V2, scores them, and generates reports

generate_top2000_compounds.py Python 3 Generates the 2,000-compound database with cost, function, and ability data; outputs JSON and CSV

rag_assembly/create_rag_index.py Python 3 Creates the RAG index, search indices, and query interface from the compound database

rag_assembly/query_example.py Python 3 Demonstrates how to query the RAG assembly programmatically

tests/test_semiconductor.py Python 3 Test suite for the semiconductor discovery and scoring modules

• Figure 1 — System Architecture Diagram
• Figure 2 — Material Discovery Method Flowchart
• Figure 3 — Scoring Algorithm Diagram
• Figure 4 — RAG Assembly Structure
• Figure 5 — System Integration Flow

Category Count Contents

Documentation 25 README, Bronze Top Thesis, Disclosure Policy, IP Notice, Patentability Analysis, Sales Pitches, Web Descriptions, Playbook, Changelog, Citation (CFF), Contact Info, Representative Image prompts and descriptions, Tiered Disclosure Strategy

Patent Documents 6 Combined patent application (DOCX), three individual method-patent drafts (MD), USPTO filing receipt (PDF — Form N-417), Application Data Sheet (PDF), Fee Worksheet (PDF — SB/0017), Oath/Declaration (PDF — AIA/0001), Micro Entity Certification (PDF — SB/0015A)

Source Code 5 Discovery engine, 2,000-compound generator, RAG index creator, query example, test suite — all Python 3

Data Files 5 2,000-compound database (JSON & CSV), RAG main index (JSON), RAG search indices (JSON), RAG query interface (JSON)

Technical Drawings 5 Five SVG figures filed with the USPTO application

Product Images 1 Representative product image (PNG)

Licence & Manifest 2 Proprietary licence (all rights reserved), MANIFEST.json

Key deliverables at a glance:

• BRONZE_TOP_THESIS.md — The complete top-discovery thesis: comprehensive analysis proving Cu-Sn is the optimal material, with full property data, cost comparisons, market-impact analysis, and manufacturing-advantage discussion
• top2000_semiconductor_compounds.json / .csv — The full 2,000-compound database with cost, function, ability, semiconductor score, and outcome-per-element data for every material
• RAG Assembly (3 JSON files) — AI-ready database with main index, six search indices, and a query-interface specification — plug directly into LangChain, vector databases, or custom AI pipelines
• Patent Application Manuscript — 50-claim non-provisional patent application (filed January 14, 2026), plus USPTO filing receipts and forms
• Python Source Code (5 scripts) — Fully functional discovery engine, database generator, RAG builder, query demo, and test suite
• Five SVG Technical Drawings — System architecture, method flowchart, scoring algorithm, RAG structure, and integration flow
• Complete Documentation Suite — IP notice, disclosure policy, patentability analysis, sales materials, valuation analysis, and engagement playbook

Intellectual-Property Notice

This package is sold without transfer of intellectual-property rights . All IP rights — including the pending patent, copyrights, and trade secrets — remain with Christopher Gabriel Brown. The buyer receives the complete source tree and worldwide commercialisation rights under a commercial source licence. Patent rights and trademarks are retained by the inventor. Commercial use beyond the granted licence requires a separate licensing agreement.

Market Context

• Global semiconductor market: US $500+ billion
• Material-discovery segment: US $10–50 billion
• AI/ML material-discovery segment: US $5–20 billion (rapidly growing)
• Likely audience: AI chip companies, fabless designers, foundries, EDA vendors, HPC sites, semiconductor R&D laboratories, material-science AI startups
• Maturity level: Research Discovery
• Discovery system: Alchemy Data V2 (Christopher Gabriel Brown)

How this is delivered

This package ships on a single Blu-ray disc, posted to the delivery address on your order. The archive on the disc is AES-256 encrypted; the passphrase is sent separately, by email, once the disc is despatched — so the disc alone is of no use to anyone who intercepts it.

Discs are despatched by tracked, signed-for post. Allow 5 business days for mastering and verification before despatch.

© 2026 Christopher Gabriel Brown

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