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Overview

General comment

Production covered

Silicon image-sensor wafer thinned and bonded to a carrier so that light enters from the back; front-end pixel-array and back-end metal patterning are not modelled in this release.

This variant represents fabrication at Sony Kumamoto/Nagasaki CIS fabs using Japan grid electricity.

This dataset represents operation with moderate PFC gas recovery and moderate water recycling.

Modelling choices

Support energy is allocated using 30,000 wafer starts per month. That capacity represents one fab phase or production module at a high-volume site, not the combined output of a multi-phase campus.

Gas abatement (POU + central scrubber) is allocated as a flat per-wafer facility charge, not scaled per etch/CVD pass.

Scope in detail

Operational scenario scope: the scenario changes fluorinated-gas emissions to air (SF6, CF4, C4F8, CHF3 and the CF4/COF2/SO2F2 abatement byproducts that scale on destroyed mass), the NET purchased quantity of the two gases wired to recovery on the purchase side rather than the emission side (neon, and EUV-lithography hydrogen where the process carries EUV passes - which is why those two purchase rows move between scenario cells), and fresh-water intake and wastewater. It does not change NF3, whose remote-plasma clean consumes the large majority of the charge inside the chamber, so the industry control is abatement of the small residue rather than recovery; nor the cryogenic HF/PF3 etch feeds, which are scrubbed rather than captured; nor any in-chamber reaction product. The utilisation and recovery fractions behind those statements are stated with the scenario definition that this dataset's name carries.

Manufacturing-region scope: the regional options of this family swap the electricity market this dataset draws from, and the regional supplier entry for those few upstream chemicals and gases whose supplier data is published by region. They change nothing else. Facility heating, ventilation and cooling energy stays on the region the process data was built for, so per-unit energy is the same across the regional options; water, process chemicals, gases, materials, process emissions and solid waste are the same as well. Every inventory row of these regional datasets is therefore expected to match. What the separate datasets carry is the background market each region draws on.

Technology
CIS BSI 300mm, image sensor
Geography
Representative region: Japan (Sony Kumamoto/Nagasaki CIS dominance).
Terms used above
  • PFC perfluorinated compounds; in power-supply contexts, power-factor correction
  • POU point of use

System boundary

System boundary - CIS BSI 300mm wafer, 300mm, JapanSystem boundary figure: identity, gate in, entering flows, the dashed system boundary and the unit processes inside it, the reference product, emissions and waste, and below it the flows that are recorded but not quantified. No inventory quantities.CIS BSI 300mm wafer, 300mm, JapanGATE INWafer fabrication from bare silicon waferENTERING FLOWSElectricityWaterProcess gasesProcess chemicalsSubstrate materialSYSTEM BOUNDARYMODELLED UNIT PROCESSES, BY CLASSOtherWafer dicingThermalLaser annealAssemblyWafer backgrind ultrathinDepositionPECVD SiONImplantIon implantTestWafer probe testREFERENCE PRODUCTCIS BSI 300mm wafer, 300mm, JapanWafer-level testing and inspectionEMISSIONS AND WASTEEmissions to airEmissions to waterWaste routesRECORDED BUT NOT QUANTIFIEDIn scope, left out of the quantified inventoryCut off below the significance threshold, or with no background dataset available - each is listed with its reason1FLOWsystem boundaryreference flowentering flowemission / waste

The figure groups this dataset's unit processes by class. It is not a count of manufacturing steps — each process runs over as many passes as the flow requires, and those pass counts ship with the dataset.

Download this figure (SVG)

Data quality and references

Composite DQI 2.3 Good
Reliability
2.5
Completeness
2.4
Temporal
2.2
Geographic
2.0
Technological
2.6
How the score is calculated: Sources used across more manufacturing steps have more influence. Sources that define assumptions for the whole model account for 77.8% of the total weighting.

Pedigree scores follow the ecoinvent data-quality matrix: 1 is the best attainable, 5 the weakest. The composite is their aggregate.

Sampling procedure
Component vendor specifications, academic papers, industry literature
Coverage status
Partial
Pedigree-scored source files
12 — the source records behind this dataset's manufacturing operations. Each carries the five pedigree axes above; the composite DQI aggregates them.

Technosphere inputs

14 flows. Quantities are not published; they ship with the dataset on Circa.

SF6 production Process gasg · -70% / +150%
Upstream REEL dataset
Sulfur hexafluoride (SF6)
Background dataset
Modelled by REEL; see the upstream dataset above.
Uncertainty
-70% / +150% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
g
NF3 production Process gasg · ±52.2%
Derivation basis
  • Calculated by the manufacturing model. The linked REEL dataset carries the upstream life cycle sources.

No source is attached to this row.

Source citations
Not stated
Upstream REEL dataset
Nitrogen trifluoride (NF3)
Background dataset
Modelled by REEL; see the upstream dataset above.
Uncertainty
±52.2% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
g
Water supply (municipal) Waterm3 · -1.7% / +1.8%
Derivation basis
  • Calculated from process-water and ultrapure-water demand across all manufacturing operations, plus facility water allocated to each finished unit. The fresh intake shown is that demand less the share reused under the water-recycling scenario this dataset assumes, and wastewater follows the same balance.

The sources below come from the manufacturing operations behind this row.

Sources (inherited)
Inherited, rolled up from the contributing process steps:
  • Investigation on Material Removal Rate in Rotation Grinding
  • SVM. "Wafer Thinning Services & Removal Rates."
  • Hapoin. "Okamoto GNX200B Specifications."
  • DISCO Corp. "DGP8761 Fully Automatic Grinder/Polisher Datasheet."
  • IMAPS/Scispace. "Optimized Die Preparation for High Yield Hybrid Bonding."
Upstream REEL dataset
Water Supply (Municipal)
Background dataset
Modelled by REEL; see the upstream dataset above.
Notes
Net fresh-water intake, supplied as municipal water
Uncertainty
-1.7% / +1.8% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
m3
Silicon wafer, single-crystal Substrate materialm2 · -7.2% / +5.9%
Derivation basis
  • Calculated from per-operation consumption, operation counts, and manufacturing yield.

No source is attached to this row.

Source citations
Not stated
Background data
ecoinvent 3.12
Background dataset
single-Si wafer, for electronics
Notes
Polished CZ wafer substrate (300mm diameter, 0.070686 m2)
Uncertainty
-7.2% / +5.9% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
m2
Electricity, Japan (JP) ElectricitykWh · -9.6% / +12.2%
Derivation basis
  • Calculated from equipment energy across all manufacturing operations, plus facility support such as cleanroom HVAC, ultrapure water, cooling, gas abatement, and bulk gases.

The sources below come from the manufacturing operations behind this row.

Sources (inherited)
Inherited, rolled up from the contributing process steps:
  • CAE Online: Teradyne Catalyst Specifications
  • EE Journal: "Advantest Unveils New Ultra-High-Current Power Supply" (2024)
  • 4Semi: TEL Precio Wafer Prober Specifications
  • FormFactor Summit 11000/12000 Facility Planning Guide
  • 3D InCites: "ERS Electronic Introduces High Power Dissipation Thermal Chuck" (2023)
  • Abachy: "How Much Energy and Water Are Required for Wafer Fabrication" (2025)
  • Investigation on Material Removal Rate in Rotation Grinding."
  • SVM. "Wafer Thinning Services & Removal Rates."
  • Hapoin. "Okamoto GNX200B Specifications."
  • DISCO Corp. "DGP8761 Fully Automatic Grinder/Polisher Datasheet."
  • IMAPS/Scispace. "Optimized Die Preparation for High Yield Hybrid Bonding."
  • Veeco LSA specifications and MDPI study
  • MDPI Photonics - 'Laser Annealing of Si Wafers Based on a Pulsed CO2 Laser', Photonics 2025, 12(4), 359
  • Applied Materials Vantage Astra and SPIE study
  • Disco Corporation DFD6361 dicing saw specifications (blade-dicing process power and wafer throughput)
Background data
ecoinvent 3.12
Background dataset
electricity, high voltage
Uncertainty
-9.6% / +12.2% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
kWh
BF3 Process gasg · -50% / +150%
Background data
ecoinvent 3.12
Background dataset
boron trifluoride
Uncertainty
-50% / +150% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
g
PH3 Process gasg · -50% / +150%
Background data
proxy-mapped
Background dataset
phosphorus, white, liquid
Uncertainty
-50% / +150% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
g
AsH3 Process gasg · -50% / +150%
Background data
ecoinvent 3.12
Background dataset
arsine
Uncertainty
-50% / +150% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
g
N2 Process gasg · -0.1% / +0.2%
Derivation basis
  • Most of this quantity is a facility nitrogen allocation rather than an operation-by-operation consumption: a per-wafer base rate for a reference fab capacity is rescaled to this model's assumed capacity through a sub-linear power law, and only the smaller per-lithography-pass and per-etch/CVD-pass purge terms follow the process flow. The base rate and the exponent are modelling estimates with no published derivation, so two models at the same technology node and wafer size can differ on this row by more than their process flows do. The uncertainty interval shown covers the per-pass terms; the facility base is a point estimate and carries none.

The sources for this row are listed below.

Source citations
  • Veeco LSA specifications and MDPI study
  • MDPI Photonics - 'Laser Annealing of Si Wafers Based on a Pulsed CO2 Laser', Photonics 2025, 12(4), 359
  • Applied Materials Vantage Astra and SPIE study
Background data
ecoinvent 3.12
Background dataset
nitrogen, liquid
Uncertainty
-0.1% / +0.2% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
g
N2O Process gasg · -93.2% / +127.3%
Source citations
  • AMAT Producer GT equipment specifications
Background data
ecoinvent 3.12
Background dataset
nitrous oxide
Uncertainty
-93.2% / +127.3% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
g
SiH4 Process gasg · -50% / +150%
Derivation basis
  • Derived from N2O:SiH4 stoichiometric flow ratio

No source is attached to this row.

Source citations
Not stated
Background data
ecoinvent 3.12
Background dataset
silicon tetrahydride
Uncertainty
-50% / +150% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
g
Compressed air Process gasg · ±33.3%
Source citations
  • CAE Online: Teradyne Catalyst Specifications
  • EE Journal: "Advantest Unveils New Ultra-High-Current Power Supply" (2024)
  • 4Semi: TEL Precio Wafer Prober Specifications
  • FormFactor Summit 11000/12000 Facility Planning Guide
  • 3D InCites: "ERS Electronic Introduces High Power Dissipation Thermal Chuck" (2023)
  • Abachy: "How Much Energy and Water Are Required for Wafer Fabrication" (2025)
Background data
ecoinvent 3.12
Background dataset
compressed air, 700 kPa gauge
Uncertainty
±33.3% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
g
O2 Process gasg
Derivation basis
  • Calculated from per-operation consumption, operation counts, and manufacturing yield.

No source is attached to this row.

Source citations
Not stated
Background data
ecoinvent 3.12
Background dataset
oxygen, liquid
Uncertainty
No range defined.
Unit
g
Natural gas, burned in industrial furnace (POU abatement, low-NOx) Process chemicalMJ · -50% / +150%
Derivation basis
  • Calculated from per-operation consumption, operation counts, and manufacturing yield.

No source is attached to this row.

Source citations
Not stated
Background data
ecoinvent 3.12
Background dataset
heat production, natural gas, at industrial furnace low-NOx >100kW
Uncertainty
-50% / +150% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
MJ

Outputs and waste

Wastewater Wastewaterm3 · -1.7% / +1.8%
Derivation basis
  • Calculated from the water balance: fresh-water input minus evaporation.

The sources below come from the manufacturing operations behind this row.

Sources (inherited)
Inherited, rolled up from the contributing process steps:
  • Investigation on Material Removal Rate in Rotation Grinding
  • SVM. "Wafer Thinning Services & Removal Rates."
  • Hapoin. "Okamoto GNX200B Specifications."
  • DISCO Corp. "DGP8761 Fully Automatic Grinder/Polisher Datasheet."
  • IMAPS/Scispace. "Optimized Die Preparation for High Yield Hybrid Bonding."
Background data
carried, no background dataset
Background dataset
No treatment route recorded for this output.
Uncertainty
-1.7% / +1.8% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
m3
Mixed process waste Solid wasteg · -1.2% / +0.9%
Derivation basis
  • Calculated from the mass balance of spent materials and consumables, with treatment selected from the waste classification.

The sources below come from the manufacturing operations behind this row.

Sources (inherited)
Inherited, rolled up from the contributing process steps:
  • Investigation on Material Removal Rate in Rotation Grinding
  • SVM. "Wafer Thinning Services & Removal Rates."
  • Hapoin. "Okamoto GNX200B Specifications."
  • DISCO Corp. "DGP8761 Fully Automatic Grinder/Polisher Datasheet."
  • IMAPS/Scispace. "Optimized Die Preparation for High Yield Hybrid Bonding."
Background data
ecoinvent 3.12 treatment route
Background dataset
treatment of inert waste, sanitary landfill
Uncertainty
-1.2% / +0.9% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
g
Spent scrubber media Solid wasteg · -95% / +50%
Background data
ecoinvent 3.12 treatment route
Background dataset
treatment of hazardous waste, underground deposit
Uncertainty
-95% / +50% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
g
Chamber deposits Solid wasteg · -24.8% / +25.2%
Derivation basis
  • Calculated from the mass balance of spent materials and consumables, with treatment selected from the waste classification.

The sources below are this dataset's own bibliography. They are not tied to this row.

Source citations (dataset-level)
Inherited from this dataset's own bibliography, not tied to this row:
  • AMAT Producer GT equipment specifications
  • IPCC 2019 Guidelines Vol 3 Ch 6
  • EPA 40 CFR Part 98 Subpart I (2024)
Background data
ecoinvent 3.12 treatment route
Background dataset
treatment of hazardous waste, underground deposit
Uncertainty
-24.8% / +25.2% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
g
Silicon Kerf Solid wasteg · ±25%
Source citations
  • Semiconductor Digest, reported blade-dicing kerf width
Background data
ecoinvent 3.12 treatment route
Background dataset
treatment of inert waste, sanitary landfill
Uncertainty
±25% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
g
Silicon Cake From Treatment Solid wasteg
Source citations
Background data
ecoinvent 3.12 treatment route
Background dataset
treatment of inert waste, sanitary landfill
Uncertainty
No range defined.
Unit
g
Scrapped material (line yield) Solid wastem2 · -72.2% / +58.8%
Background data
ecoinvent 3.12 treatment route
Background dataset
treatment of waste electric and electronic equipment, shredding
Notes
Calculated reject material after accounting for a manufacturing yield of 90%. Wafer starts that do not complete processing, at the modeled line yield of 90 percent. Line yield here counts test, monitor, and damaged wafers against wafers processed - the inclusive convention of the cited fab benchmarking and life cycle studies. The 0.85 to 0.97 uncertainty band on the yield spans measured multi-fab benchmarking: the low end reflects below-average lines and deep multi-layer flows; the high end reflects the best benchmarked lines, which reach about 97 to 98 percent.
Uncertainty
-72.2% / +58.8% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
m2

Emissions to air

4 elementary flows released to air by this dataset's own operations. Quantities are not published; they ship with the dataset on Circa.

SF6 Emission to airg
Compartment
Air (non-urban air or from high stacks)
CAS number
2551-62-4
ecoinvent 3.12 elementary flow
Sulfur hexafluoride
Uncertainty
A minimum-maximum range is defined for this flow. Bounds ship with the dataset on Circa.
Unit
g
N2O Emission to airg
Source citations
  • AMAT Producer GT equipment specifications
Compartment
Air (non-urban air or from high stacks)
CAS number
10024-97-2
ecoinvent 3.12 elementary flow
Dinitrogen monoxide
Uncertainty
A minimum-maximum range is defined for this flow. Bounds ship with the dataset on Circa.
Unit
g
NF3 Emission to airg
Derivation basis
  • Calculated as an air release, from a mass balance on the process gases going in, with the destruction or removal efficiency of any point-of-use abatement applied.

No source is attached to this row.

Source citations
Not stated
Compartment
Air (non-urban air or from high stacks)
CAS number
7783-54-2
ecoinvent 3.12 elementary flow
Nitrogen fluoride
Uncertainty
A minimum-maximum range is defined for this flow. Bounds ship with the dataset on Circa.
Unit
g
F2 Emission to airg
Derivation basis
  • Calculated as an air release, from a mass balance on the process gases going in, with the destruction or removal efficiency of any point-of-use abatement applied.

No source is attached to this row.

Source citations
Not stated
Compartment
Air (non-urban air or from high stacks)
CAS number
7782-41-4
ecoinvent 3.12 elementary flow
Fluorine
Uncertainty
A minimum-maximum range is defined for this flow. Bounds ship with the dataset on Circa.
Unit
g

Emissions to water

4 elementary flows released to water by this dataset's own operations. Quantities are not published; they ship with the dataset on Circa.

Si Emission to waterg
Source citations (dataset-level)
Source citations (dataset-level)
Inherited from this dataset's own bibliography, not tied to this row:
  • Investigation on Material Removal Rate in Rotation Grinding
  • SVM. "Wafer Thinning Services & Removal Rates."
  • Hapoin. "Okamoto GNX200B Specifications."
  • DISCO Corp. "DGP8761 Fully Automatic Grinder/Polisher Datasheet."
  • IMAPS/Scispace. "Optimized Die Preparation for High Yield Hybrid Bonding."
Compartment
Water (surface water)
CAS number
7440-21-3
ecoinvent 3.12 elementary flow
Silicon
Uncertainty
A minimum-maximum range is defined for this flow. Bounds ship with the dataset on Circa.
Unit
g
F- Emission to waterg
Derivation basis
  • Mass balance from BF3 input

The sources below are this dataset's own bibliography. They are not tied to this row.

Source citations (dataset-level)
Inherited from this dataset's own bibliography, not tied to this row:
Compartment
Water (surface water)
CAS number
16984-48-8
ecoinvent 3.12 elementary flow
Fluoride
Uncertainty
No range defined.
Unit
g
PO4 3- Emission to waterg
Derivation basis
  • Mass balance from PH3 input

The sources below are this dataset's own bibliography. They are not tied to this row.

Source citations (dataset-level)
Inherited from this dataset's own bibliography, not tied to this row:
Compartment
Water (surface water)
Formula
PO43-
CAS number
14265-44-2
ecoinvent 3.12 elementary flow
Phosphate
Uncertainty
No range defined.
Unit
g
As Emission to waterg
Derivation basis
  • Mass balance from AsH3 input

The sources below are this dataset's own bibliography. They are not tied to this row.

Source citations (dataset-level)
Inherited from this dataset's own bibliography, not tied to this row:
Compartment
Water (surface water)
CAS number
7440-38-2
ecoinvent 3.12 elementary flow
Arsenic ion
Uncertainty
No range defined.
Unit
g

Flows not quantified

1 flow is recorded for this dataset but carries no quantity — cut off below the significance threshold, or with no background dataset available. It remains inside the declared system boundary; each is listed with its reason.

Not quantified - no background dataset available (1)

Scrubber media (dry-bed chemisorbent) Process chemicalg · -95% / +50%
Background data
no background dataset
Background dataset
Not applicable: this flow is not quantified in the inventory.
Reason
Purchased granulate for point-of-use dry-bed abatement of hydride process gases. No chemisorbent or sorbent abatement-medium activity exists in the background database used for linkage. Its burden is dominated by the impregnated reactive phase that makes it a scavenger, and vendors do not disclose that chemistry, so a generic carbon-supported substitute would carry the wrong material. The quantity is recorded here and its upstream production is left unlinked.
Uncertainty
-95% / +50% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit
g

Process steps deferred in this release (5)

Steps of this dataset's process flow that are declared but carry no quantified inventory in this release. Each is listed with its reason.

Pixel Array FEOL (not modelled)
Deferred pending public data on Pixel Array FEOL.
Oxide/Metal BEOL (not modelled)
Deferred pending public data on Oxide/Metal BEOL.
Carrier Wafer Bonding (not modelled)
Deferred pending public data on Carrier Wafer Bonding.
Color Filter Array (not modelled)
Deferred pending public data on Color Filter Array.
Microlens Array (not modelled)
Deferred pending public data on Microlens Array.

Limitations and unquantified flows (3)

Limits this dataset declares about itself: first any limit stated in its own description, then any limit it declares flow by flow, grouped by channel. Each entry below is the model's own disclosure.

From the dataset description

  • Carrier-wafer bonding, colour-filter, and microlens steps are also declared deferred.

General

  • These process steps in the modelled flow are declared not modelled and contribute nothing to this inventory: Pixel Array FEOL; Oxide/Metal BEOL; Carrier Wafer Bonding; Color Filter Array; Microlens Array.
  • Some consumable pulls referenced by the modelled process steps did not resolve to an inventory row and are absent from this dataset. They are itemised in the dataset's own export audit.

The flows above are this dataset's own records. What follows are the scope rules set once for the whole database in the methodology report, repeated here so every dataset page carries them.

Database-wide boundary policy — applies to every REEL dataset

These boundaries are set once for the whole database, in Chapter 2 of the methodology report, and apply to this dataset wherever they are relevant to it. The excluded flows listed above are specific to this dataset.

Use phase
Product operation is outside the cradle-to-gate scope.
End-of-life treatment
Recycling and disposal are outside the cradle-to-gate scope.
Distribution and retail
The gate is a finished component ready for integration into a higher-level assembly.
Inbound transport of raw materials
Transport of purchased raw materials to the manufacturing facility is already inside the upstream "market for" datasets that users link to a background database, so it is not modelled a second time here. This does not cover freight between REEL production stages, which is modelled where a dataset authors it.
Returnable shipping containers
Where freight between production stages is modelled, the mass moved is the product itself. The shipping container (FOSB, SEMI M31) is returnable capital equipment whose per-trip share is unsourced, so its tare is excluded from the transport effort.
Photomask fabrication
A mask set's embodied burden is amortised across a high-volume production run and is not attributed per wafer. Users assessing low-volume production should add mask fabrication separately; the methodology report gives the basis for the exclusion.
Employee transport, administration and R&D overhead
Employee transportation, facility administration and R&D/pilot-production overhead are outside scope.
Capital goods
Manufacturing equipment, cleanroom construction and facility infrastructure are excluded, on the grounds of absent public data on equipment embodied energy, uncertainty in equipment lifetime and allocation, common practice in electronics LCA, and a focus on the operational inventory. Future versions may include capital goods when sufficient public data becomes available.
Precious metal recovery credits
Scrap recovery credits for precious metals are excluded pending data availability.
Wafer reclaim
Test wafers and scrap are outside the system boundary.

Inside the boundary, linked rather than modelled

Silicon ingot growth and wafer slicing
Inside the cradle-to-gate scope, but treated as upstream material inputs linked to background databases rather than modelled as REEL processes.
Freight between production stages
Where a dataset's product moves between REEL production stages - wafer fabrication to the packaging site, for example - that leg is authored as a transport service and linked to an ecoinvent freight activity. It is measured as a transport effort in tonne-kilometres, not as a mass. Route distances are authored per route class with a stated band; a lower bound of zero is a modelling statement that the two sites can be co-located, not a missing value.

Cut-off criteria

A flow is excluded from a process inventory when it contributes less than 1 % of the total mass of inputs to that unit process, or less than 1 % of its total energy input. The denominator is total process inputs, not product mass. That distinction matters in semiconductor manufacturing, where the input mass of water, chemicals and gases greatly exceeds the product mass, so the threshold removes only genuinely minor flows.

Included regardless of the cut-off

  • Perfluorocarbons (CF4, C2F6, SF6, NF3) - high GWP, EPA regulated
  • Heavy metals (Pb, Cd, Hg, Cr(VI)) - RoHS regulated, high toxicity
  • Volatile organics (photoresist solvents, PGMEA) - air quality
  • Precious metals (Au, Ag, Pd, Pt) - high embodied impacts
  • Ozone-depleting substances (legacy CFCs, HCFCs) - Montreal Protocol