Toward robust Life Cycle Assessment of nanoelectronics: Resolving database gaps with transparent and reusable input datasets.

The nanoelectronics sector relies on complex combinations of photoresists, developers, stripping agents, high-purity solvents, and metal-organic precursors used in lithography and atomic layer deposition (ALD). However, many of these substances are not represented as standalone unit processes in current Life Cycle Inventory (LCI) databases (e.g., Ecoinvent), forcing practitioners to rely on generic proxy datasets or to omit relevant contributors, thereby undermining the robustness and transparency of Life Cycle Assessment (LCA) applied to nanoelectronics manufacturing. In this work, a set of new cradle-to-gate LCI datasets was developed for ten representative materials widely used in lithography and ALD processes: i) the novolak-based photoresist AZ 1518 , ii) the metal-ion-free developer AZ 726 MIF , iii) the lift-off resist LOR 5A , iv) the positive-tone photoresist PR S1813 , v) the epoxy-based negative resist SU-8 , vi) the photoresist stripping solvent N-methyl-2-pyrrolidone ( NMP ), vii) the casting solvent propylene glycol monomethyl ether acetate ( PGMEA ), viii) the cationic photoacid generator TAS·SbF 6 ( PAG ), ix) the ALD precursor tetrakis(dimethylamido)hafnium ( TDMAH ), and x) the ALD precursor tetrakis(dimethylamido)zirconium ( TDMAZ ). Each dataset was modelled as a single unit process with a functional unit (FU) of 1 kg of product and cradle-to-gate system boundaries, covering synthesis, formulation, purification, and associated material and energy inputs. The reconstruction of process flowcharts and inventories was supported by a systematic review of 322 scientific and technical sources (including patents, peer-reviewed articles, technical documentation, and industrial process descriptions), ensuring transparency and reproducibility of the datasets. The datasets were subsequently harmonized using standardized units, consistent allocation procedures, and pedigree-matrix-based uncertainty scoring. Environmental impacts were quantified using the ReCiPe 2016 Midpoint (H) method. Global warming impacts ranged from 0.66-1.30 kg CO 2 -eq kg -1 for AZ 726 MIF to 18.70-34.93 kg CO 2 -eq kg -1 for TDMAH, with central values reaching 26.81 kg CO 2 -eq kg -1 for this precursor. Mineral resource scarcity impacts also varied substantially across materials, ranging from 1.84·10 -3 -3.71·10 -3 kg Cu-eq for AZ 726 MIF to 0.79-1.66 kg Cu-eq for TDMAZ, reflecting the influence of metal supply chains and the resource intensity of high-purity organometallic precursor synthesis. Land-use indicators were likewise influenced by precursor synthesis and purification, with TDMAZ showing the highest central value (1.17 m 2 a crop-eq kg -1 ) due to upstream burdens associated with zirconium production and refining. Overall, the results show that upstream specialty chemistry and high-purity precursor production dominate cradle-to-gate environmental profiles, whereas downstream formulation steps such as blending, filtration, and packaging contribute comparatively modest loads. The datasets developed in this work fill critical LCI gaps and provide transparent, reusable building blocks to support more realistic modelling of nanoelectronics processes, early-stage eco-design, and sensitivity analyses.