Overview

General comment

Production covered

Chip ferrite bead for EMI (electromagnetic interference) suppression. Multilayer construction with Ag internal electrodes and Ni-Zn or Mn-Zn ferrite body. Provides high impedance at RF frequencies while low DC resistance. Critical for EMC compliance in all electronics.

Modelling choices

This dataset is modelled with no water reuse. No reclaim rate is published for this kind of plant, so the water shown is the full fresh intake and no recycling credit has been deducted from it.

Technology: Multilayer Chip Inductor, EMI Suppression
Geography: Global average data from equipment vendor specifications and industry literature

System boundary

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 2.9 Good

Reliability

2.6

Completeness

3.2

Temporal

2.6

Geographic

2.8

Technological

3.1

How the score is calculated: Sources used across more manufacturing steps have more influence. Sources that define assumptions for the whole model account for 50.0% 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, industry literature
Coverage status: Partial
Pedigree-scored source files: 2 — the source records behind this dataset's manufacturing operations. Each carries the five pedigree axes above; the composite DQI aggregates them.

Technosphere inputs

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

Electricity, China (CN) ElectricitykWh · ±30%

Derivation basis: Calculated from equipment energy across all manufacturing operations. It also covers the facility support this model includes: cleanroom HVAC, cooling water, bulk gases and wastewater treatment.

No source is attached to this row.
Source citations: Not stated
Background data: ecoinvent 3.12
Background dataset: electricity, medium voltage
Uncertainty: ±30% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit: kWh

Electricity, China (CN) - Cleanroom HVAC ElectricitykWh · -60% / +0%

Derivation basis: Calculated from equipment energy across all manufacturing operations. It also covers the facility support this model includes: cleanroom HVAC, cooling water, bulk gases and wastewater treatment.

The sources for this row are listed below.
Source citations: osti.gov (date not recorded). Performance Evaluation of Cleanroom Environmental Systems. Government report.
osti.gov (date not recorded). Airflow Design for Cleanrooms. Government report.
aceee.org (date not recorded). Development of Cleanroom Energy Benchmarks. Conference paper.
escholarship.org (date not recorded). Cleanrooms Energy Efficiency: Metrics and Benchmarks. Government report.
Background data: ecoinvent 3.12
Background dataset: electricity, medium voltage
Uncertainty: -60% / +0% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit: kWh

Electricity, China (CN) - Cooling Water ElectricitykWh · -33.3% / +25%

Derivation basis: Calculated from equipment energy across all manufacturing operations. It also covers the facility support this model includes: cleanroom HVAC, cooling water, bulk gases and wastewater treatment.

The sources for this row are listed below.
Source citations: osti.gov (date not recorded). Performance Evaluation of Cleanroom Environmental Systems. Government report.
escholarship.org (date not recorded). Cleanrooms Energy Efficiency: Metrics and Benchmarks. Government report.
Background data: ecoinvent 3.12
Background dataset: electricity, medium voltage
Uncertainty: -33.3% / +25% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit: kWh

Electricity, China (CN) - Waste Treatment ElectricitykWh · -50% / +25%

Derivation basis: Calculated from equipment energy across all manufacturing operations. It also covers the facility support this model includes: cleanroom HVAC, cooling water, bulk gases and wastewater treatment.

The sources for this row are listed below.
Source citations: yageogroup.com (date not recorded). YAGEO 2023 Sustainability Report. Corporate report.
Background data: ecoinvent 3.12
Background dataset: electricity, medium voltage
Uncertainty: -50% / +25% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit: kWh

Electricity, China (CN) - Bulk Gas Supply ElectricitykWh · -60% / +150%

Derivation basis: Calculated from equipment energy across all manufacturing operations. It also covers the facility support this model includes: cleanroom HVAC, cooling water, bulk gases and wastewater treatment.

The sources for this row are listed below.
Source citations: yageogroup.com (date not recorded). YAGEO 2023 Sustainability Report. Corporate report.
Background data: ecoinvent 3.12
Background dataset: electricity, medium voltage
Uncertainty: -60% / +150% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit: kWh

Fresh water WaterL · ±25%

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 whole demand: the water-recycling scenario this dataset assumes reuses none of it, so nothing has been deducted, and wastewater follows the same balance.

No source is attached to this row.
Source citations: Not stated
Background data: ecoinvent 3.12
Background dataset: tap water
Notes: Process and cleaning water
Uncertainty: ±25% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit: L

Cooling tower makeup water WaterL · -33.3% / +25%

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 whole demand: the water-recycling scenario this dataset assumes reuses none of it, so nothing has been deducted, and wastewater follows the same balance.

The sources for this row are listed below.
Source citations: osti.gov (date not recorded). Performance Evaluation of Cleanroom Environmental Systems. Government report.
escholarship.org (date not recorded). Cleanrooms Energy Efficiency: Metrics and Benchmarks. Government report.
Background data: ecoinvent 3.12
Background dataset: tap water
Uncertainty: -33.3% / +25% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit: L

Binder PVB Process chemicalg

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: proxy-mapped
Background dataset: ethylene vinyl acetate copolymer
Uncertainty: No range defined.
Unit: g

Plasticizer Process chemicalg

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: proxy-mapped
Background dataset: dioctyl terephthalate
Uncertainty: No range defined.
Unit: g

Solvent Process chemicalmL

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: solvent, organic
Uncertainty: No range defined.
Unit: mL

Ni Sulfate Process chemicalmg

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: nickel sulfate
Uncertainty: No range defined.
Unit: mg

Sn Sulfate Process chemicalmg

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: proxy-mapped
Background dataset: tin
Notes: Proxy mapping uses 55.3% of the material mass.
Uncertainty: No range defined.
Unit: mg

Iron ore concentrate Materialkg

Derivation basis: Derived from industry datasheets

No source is attached to this row.
Source citations: Not stated
Background data: proxy-mapped
Background dataset: iron ore concentrate
Notes: From Ferrite Body (70.0% iron oxide). Purchased input grossed to fund the authored process loss booked as Green Tape Scrap, Dicing Waste (mass-closure funding; the retained mass is the product-side figure).
Uncertainty: No range defined.
Unit: kg

Nickel, class 1 Materialkg

Derivation basis: Derived from industry datasheets

No source is attached to this row.
Source citations: Not stated
Background data: ecoinvent 3.12
Background dataset: nickel, class 1
Notes: From Ferrite Body (15.0% nickel oxide). NiO precursor - model as Ni. Purchased input grossed to fund the authored process loss booked as Green Tape Scrap, Dicing Waste (mass-closure funding; the retained mass is the product-side figure).
Uncertainty: No range defined.
Unit: kg

Zinc oxide Materialkg

Derivation basis: Derived from industry datasheets

No source is attached to this row.
Source citations: Not stated
Background data: ecoinvent 3.12
Background dataset: zinc oxide
Notes: From Ferrite Body (15.0% zinc oxide). Purchased input grossed to fund the authored process loss booked as Green Tape Scrap, Dicing Waste (mass-closure funding; the retained mass is the product-side figure).
Uncertainty: No range defined.
Unit: kg

Ag Electrodes Materialkg

Derivation basis: Derived from industry datasheets

No source is attached to this row.
Source citations: Not stated
Background data: ecoinvent 3.12
Background dataset: silver
Uncertainty: No range defined.
Unit: kg

Termination Ag Materialkg

Derivation basis: Derived from industry datasheets

No source is attached to this row.
Source citations: Not stated
Background data: ecoinvent 3.12
Background dataset: silver
Uncertainty: No range defined.
Unit: kg

Ni Sn Plating Materialkg

Derivation basis: Derived from industry datasheets

No source is attached to this row.
Source citations: Not stated
Background data: ecoinvent 3.12
Background dataset: nickel, class 1
Notes: Purchased input grossed to fund the authored process loss booked as Plating Sludge (mass-closure funding; the retained mass is the product-side figure).
Uncertainty: No range defined.
Unit: kg

Outputs and waste

Cooling tower blowdown Wastewaterm3 · -33.3% / +25%

Source citations: osti.gov (date not recorded). Performance Evaluation of Cleanroom Environmental Systems. Government report.
escholarship.org (date not recorded). Cleanrooms Energy Efficiency: Metrics and Benchmarks. Government report.
Background data: carried, no background dataset
Background dataset: No treatment route recorded for this output.
Uncertainty: -33.3% / +25% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit: m3

Waste treatment Wastewaterm3 · ±25%

Source citations: yageogroup.com (date not recorded). YAGEO 2023 Sustainability Report. Corporate report.
Background data: carried, no background dataset
Background dataset: No treatment route recorded for this output.
Uncertainty: ±25% around the published quantity. The bounds themselves ship with the dataset on Circa.
Unit: m3

Green Tape Scrap Solid wastemg

Derivation basis: Calculated from the mass balance of spent materials and consumables, with treatment selected from the waste classification.

No source is attached to this row.
Source citations: Not stated
Background data: ecoinvent 3.12 treatment route
Background dataset: treatment of inert waste, sanitary landfill
Uncertainty: No range defined.
Unit: mg

Dicing Waste Solid wastemg

Derivation basis: Calculated from the mass balance of spent materials and consumables, with treatment selected from the waste classification.

No source is attached to this row.
Source citations: Not stated
Background data: ecoinvent 3.12 treatment route
Background dataset: treatment of inert waste, sanitary landfill
Uncertainty: No range defined.
Unit: mg

Plating Sludge Solid wastemg

Derivation basis: Calculated from the mass balance of spent materials and consumables, with treatment selected from the waste classification.

No source is attached to this row.
Source citations: Not stated
Background data: ecoinvent 3.12 treatment route
Background dataset: market for metalliferous hydroxide sludge
Uncertainty: No range defined.
Unit: mg

Sintered Rejects Solid wastekg

Derivation basis: Calculated from the mass balance of spent materials and consumables, with treatment selected from the waste classification.

No source is attached to this row.
Source citations: Not stated
Background data: ecoinvent 3.12 treatment route
Background dataset: treatment of inert waste, sanitary landfill
Notes: Calculated reject material after accounting for the model's declared 98% manufacturing yield, applied to the material quantity this row is taken from.
Uncertainty: No range defined.
Unit: kg

Emissions to air

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

VOCs Emission to airg

Derivation basis: Authored at the model: emission factor declared by the model itself, converted from its authored unit to grams per functional unit. It is not re-derived from the process-gas inputs and no point-of-use abatement is applied to it; the model's own notes and sources carry the factor's basis

No source is attached to this row.
Source citations: Not stated
Compartment: Air (non-urban air or from high stacks)
ecoinvent 3.12 elementary flow: NMVOC, non-methane volatile organic compounds
Uncertainty: No range defined.
Unit: g

Emissions to water

No emissions to water are recorded at this level.

Flows not quantified

3 flows are recorded for this dataset but carry no quantity — cut off below the significance threshold, or with no background dataset available. They remain inside the declared system boundary; each is listed with its reason.

Not modelled (3)

Compressed dry air Process gasm3

Derivation basis: Not stated
Source citations: Not stated
Background data: not modelled
Background dataset: Not applicable: this flow is not quantified in the inventory.
Reason: Compressed dry air is a known facility input for this category; no allocation driver has been established, so it is recorded but not quantified - deferred, not judged immaterial.
Uncertainty: No range defined.
Unit: m3

Facility gas mass Process gaskg

Derivation basis: Not stated
Source citations: Not stated
Background data: not modelled
Background dataset: Not applicable: this flow is not quantified in the inventory.
Reason: Facility gas mass is a known facility input for this category; no allocation driver has been established, so it is recorded but not quantified - deferred, not judged immaterial.
Uncertainty: No range defined.
Unit: kg

Vehicle Process chemicalmL

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: not modelled
Background dataset: Not applicable: this flow is not quantified in the inventory.
Reason: Proprietary plating or printing additive consumed in trace quantity, recorded in this inventory. Formulations vary by supplier and no representative public production dataset exists, so the upstream burden is left unlinked rather than approximated with an unrepresentative proxy.
Uncertainty: No range defined.
Unit: mL

Limitations and unquantified flows (1)

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.

These flows are named in this inventory but ship without a quantity, so their burden is not carried anywhere in the dataset: Compressed dry air; Facility gas mass. Each row states its own reason on the row itself.

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