#!/usr/bin/env python3 """Educational surface-treatment model summaries for SemiAgora process replays. The generated datasets teach surface-cleaning observables such as contact angle, organic-residue proxy, hydroxylation proxy, and recovery after air exposure. They do not contain a qualified recipe, tool setting, or facility procedure. """ from __future__ import annotations import json import math from pathlib import Path ROOT = Path(__file__).resolve().parent SOURCES = { "harrick": { "label": "Harrick Plasma surface chemistry modification", "url": "https://harrickplasma.com/surface-chemistry/", "note": "Public application note for plasma-driven surface chemistry, wettability, hydroxylation, and air recovery boundaries.", }, "uiuc_uv_ozone": { "label": "UIUC MRL UV/Ozone Cleaner", "url": "https://mrl.illinois.edu/facilities/equipment/uvozone-cleaner", "note": "Public cleanroom equipment page describing UV/O3 organic-contaminant removal, hydrophilicity, and deposition-prep use.", }, "unc_o2_plasma": { "label": "UNC CHANL oxygen plasma system", "url": "https://chanl.unc.edu/instrument/oxygen-plasma-system/", "note": "Public cleanroom instrument page listing surface activation, cleaning, wettability alteration, and adhesion preparation.", }, "samco_uv_ozone": { "label": "Samco UV ozone surface treatment", "url": "https://www.samcointl.com/processes/surface-treatment/uv-ozone/", "note": "Public industry page for UV/O3 surface treatment and organic-contaminant removal vocabulary.", }, } def decay_to_floor(initial: float, floor: float, rate: float, dose: float) -> float: return floor + (initial - floor) * math.exp(-rate * dose) def round_list(values: list[float], digits: int = 3) -> list[float]: return [round(value, digits) for value in values] def build_o2_plasma() -> dict: time_s = [0, 5, 10, 20, 30, 60, 120] configs = [ ("gentle-o2-plasma", 0.55, "lower ion exposure, slower activation"), ("nominal-o2-plasma", 1.00, "fast hydrophilic activation teaching reference"), ("extended-o2-plasma", 1.60, "strong activation with louder ion-exposure warning"), ] cases = [] for case_id, dose_scale, intent in configs: contact = [decay_to_floor(72.0, 6.0, 0.11, t * dose_scale) for t in time_s] hydroxyl = [100.0 * (1 - math.exp(-0.075 * t * dose_scale)) for t in time_s] organic_residue = [100.0 * math.exp(-0.055 * t * dose_scale) for t in time_s] risk = [100.0 * (1 - math.exp(-t * dose_scale / 210.0)) for t in time_s] cases.append( { "id": case_id, "intent": intent, "normalized_rf_dose": dose_scale, "time_s": time_s, "water_contact_angle_deg": round_list(contact, 2), "hydroxylation_proxy_percent": round_list(hydroxyl, 1), "organic_residue_proxy_percent": round_list(organic_residue, 1), "ion_exposure_risk_index": round_list(risk, 1), } ) age_h = [0, 0.25, 1, 4, 24, 72] immediate_angle = cases[1]["water_contact_angle_deg"][5] recovered_angle = 54.0 recovery = [ immediate_angle + (recovered_angle - immediate_angle) * (1 - math.exp(-math.sqrt(max(h, 0.0)) / 2.7)) for h in age_h ] return { "schema": "semiagora.process-surface-treatment.v1", "experiment_id": "SA-PROC-SURF-O2-PLASMA-001", "title": "O2 plasma surface activation replay", "execution_mode": "precomputed-only", "engine": "SemiAgora surface-treatment kinetics model", "model_boundary": "Educational contact-angle and residue-proxy replay; not a plasma tool recipe, wafer qualification, or measured SemiAgora process.", "observables": [ "water contact angle", "hydroxylation proxy", "organic residue proxy", "ion exposure risk index", "hydrophobic recovery after air exposure", ], "cases": cases, "air_recovery": { "after_nominal_60_s_treatment": True, "age_h": age_h, "water_contact_angle_deg": round_list(recovery, 2), "note": "Recovery is included so learners do not treat activated surfaces as permanent.", }, "derived_metrics": { "nominal_contact_angle_30_s_deg": cases[1]["water_contact_angle_deg"][4], "nominal_contact_angle_60_s_deg": cases[1]["water_contact_angle_deg"][5], "nominal_organic_removal_60_s_percent": round(100 - cases[1]["organic_residue_proxy_percent"][5], 1), "nominal_recovered_contact_angle_24_h_deg": round_list(recovery, 2)[4], "extended_ion_exposure_risk_120_s_index": cases[2]["ion_exposure_risk_index"][-1], }, "verification": { "cases": len(cases), "contact_angle_decreases_with_time": all( all(a >= b for a, b in zip(case["water_contact_angle_deg"], case["water_contact_angle_deg"][1:])) for case in cases ), "organic_residue_decreases_with_time": all( all(a >= b for a, b in zip(case["organic_residue_proxy_percent"], case["organic_residue_proxy_percent"][1:])) for case in cases ), "risk_increases_with_time": all( all(a <= b for a, b in zip(case["ion_exposure_risk_index"], case["ion_exposure_risk_index"][1:])) for case in cases ), "air_recovery_increases_contact_angle": all(a <= b for a, b in zip(round_list(recovery, 2), round_list(recovery, 2)[1:])), }, "sources": [SOURCES["harrick"], SOURCES["unc_o2_plasma"], SOURCES["uiuc_uv_ozone"]], "limitations": [ "No plasma density, sheath physics, ion energy distribution, chamber pressure, gas flow, sample temperature, endpoint signal, or material-specific etch rate is modeled.", "The RF-dose labels are normalized teaching axes and must not be copied into facility settings.", "Contact-angle values are synthetic teaching replays, not measured wafer data.", "Real use requires material compatibility, charging and damage review, metrology, safety approval, and tool-specific training.", ], } def build_o3_ozone() -> dict: time_min = [0, 1, 3, 5, 10, 15, 20, 30] configs = [ ("dry-uv-o3", 1.0, "UV/O3 dry clean and activation replay"), ("di-o3-water", 0.7, "ozonated-water organic removal teaching replay"), ] cases = [] for case_id, strength, intent in configs: floor = 10.0 if case_id == "dry-uv-o3" else 18.0 contact = [decay_to_floor(72.0, floor, 0.25, t * strength) for t in time_min] organic_residue = [100.0 * math.exp(-0.22 * t * strength) for t in time_min] hydroxyl = [100.0 * (1 - math.exp(-0.20 * t * strength)) for t in time_min] oxide_proxy = [0.015 * t * strength + 0.18 * (1 - math.exp(-0.20 * t * strength)) for t in time_min] cases.append( { "id": case_id, "intent": intent, "normalized_oxidant_strength": strength, "time_min": time_min, "water_contact_angle_deg": round_list(contact, 2), "organic_residue_proxy_percent": round_list(organic_residue, 1), "hydroxylation_proxy_percent": round_list(hydroxyl, 1), "oxide_growth_proxy_nm": round_list(oxide_proxy, 3), } ) dry = cases[0] water = cases[1] return { "schema": "semiagora.process-surface-treatment.v1", "experiment_id": "SA-PROC-SURF-O3-OZONE-001", "title": "O3 ozone and UV-ozone surface clean replay", "execution_mode": "precomputed-only", "engine": "SemiAgora surface-treatment kinetics model", "model_boundary": "Educational ozone/UV-O3 organic-removal and activation replay; not an ozone process recipe, wafer qualification, or measured SemiAgora process.", "observables": [ "water contact angle", "organic residue proxy", "hydroxylation proxy", "oxide growth proxy", ], "cases": cases, "derived_metrics": { "dry_uv_o3_contact_angle_15_min_deg": dry["water_contact_angle_deg"][5], "dry_uv_o3_organic_removal_15_min_percent": round(100 - dry["organic_residue_proxy_percent"][5], 1), "dry_uv_o3_oxide_proxy_15_min_nm": dry["oxide_growth_proxy_nm"][5], "di_o3_water_contact_angle_15_min_deg": water["water_contact_angle_deg"][5], }, "verification": { "cases": len(cases), "contact_angle_decreases_with_time": all( all(a >= b for a, b in zip(case["water_contact_angle_deg"], case["water_contact_angle_deg"][1:])) for case in cases ), "organic_residue_decreases_with_time": all( all(a >= b for a, b in zip(case["organic_residue_proxy_percent"], case["organic_residue_proxy_percent"][1:])) for case in cases ), "oxide_proxy_increases_with_time": all( all(a <= b for a, b in zip(case["oxide_growth_proxy_nm"], case["oxide_growth_proxy_nm"][1:])) for case in cases ), }, "sources": [SOURCES["uiuc_uv_ozone"], SOURCES["samco_uv_ozone"], SOURCES["harrick"]], "limitations": [ "No UV intensity distribution, ozone concentration, humidity, substrate temperature, dissolved ozone transport, or material-specific oxidation rate is modeled.", "The ozone-strength labels are normalized teaching axes and must not be copied into equipment settings.", "The oxide-growth values are proxies for caution, not measured film thickness.", "Real use requires material compatibility, contamination metrology, safety approval, and tool-specific training.", ], } def write_payload(name: str, payload: dict) -> None: (ROOT / name).write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8") def main() -> None: o2 = build_o2_plasma() o3 = build_o3_ozone() assert o2["verification"]["contact_angle_decreases_with_time"] assert o2["verification"]["organic_residue_decreases_with_time"] assert o2["verification"]["risk_increases_with_time"] assert o3["verification"]["contact_angle_decreases_with_time"] assert o3["verification"]["organic_residue_decreases_with_time"] assert o3["verification"]["oxide_proxy_increases_with_time"] write_payload("process-o2-plasma-surface-activation-web-v1.json", o2) write_payload("process-o3-ozone-surface-clean-web-v1.json", o3) print(json.dumps({"o2": o2["derived_metrics"], "o3": o3["derived_metrics"]}, indent=2)) if __name__ == "__main__": main()