# Methodological Challenges in Conducting a Screening Life Cycle Assessment of Dense Electronic Devices: The Case of Smartphones

Benjamin Chedotel, Thibaut Marty, François Berry, Maxime Pelcat

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Benjamin Chedotel, Thibaut Marty, François Berry, Maxime Pelcat. Methodological Challenges in Conducting a Screening Life Cycle Assessment of Dense Electronic Devices: The Case of Smartphones. 22nd ACM International Conference on Computing Frontiers (CF' 25), May 2025, Cagliari, Italy. pp.121-129, 10.1145/3706594.3727964. hal-05157002

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# Methodological Challenges in Conducting a Screening Life Cycle Assessment of Dense Electronic Devices: The Case of Smartphones

## Benjamin Chedotel

Univ Rennes, INSA Rennes, CNRS, IETR - UMR 6164
Rennes, France
benjamin.chedotel@insa-rennes.fr

## François Berry

Institut Pascal, Université Clermont-Auvergne, CNRS,
SIGMA Clermont
Clermont-Ferrand, France
francois.berry@uca.fr

## Abstract

Environmental Life Cycle Assessment (LCA) is becoming more widely adopted to evaluate the environmental impacts of devices and services across their manufacturing, use, and end-of-life phases. LCAs are usually conducted in several steps. After determining the goal and scope of the study, a first quick evaluation of impacts, called *screening LCA*, aims at determining which activities and components significantly affect the impacts. Then, a cut-off method is applied that concentrates next LCA steps on activities and components representing more than a minimum share of impacts. Finally, a complete study is performed including precise life cycle inventory, measurements, impact assessment and interpretation. Screening LCAs are conducted in a few hours using generic environmental impact databases. The cut-off is a threshold that determines which components are considered in the final LCA based on their cumulative estimated impact (usually 3% of a total impact, e.g. carbon impact). The set of least impacting components below this cut-off are excluded, in order to streamline the analysis process.

This paper concentrates on embodied carbon impacts of electronic devices, i.e. impact of manufacturing. This embodied carbon tends to dominate use and end-of-life phases in battery-powered devices. We demonstrate the difficulties of conducting a screening LCAs for assessing the impact of dense electronic systems such as smartphones and tablets, and propose a method for overcoming these difficulties. Indeed, the reliability of the cut-off method can be compromised by rough estimations and potential inaccuracies inherent to screening LCAs. We demonstrate on LCAs of the Apple iPhone 16 and Samsung Galaxy Note II Printed Circuit Board Assemblies (PCBAs) that screening LCAs tend to over-estimate and under-estimate different impacts, leading to the misidentification of significant components and to a flawed cut-off that cannot be

## Thibaut Marty

Univ Rennes, INSA Rennes, CNRS, IETR - UMR 6164
Rennes, France
thibaut.marty@insa-rennes.fr

## Maxime Pelcat

Univ Rennes, INSA Rennes, CNRS, IETR - UMR 6164
Rennes, France
maxime.pelcat@insa-rennes.fr

corrected by subsequent steps. We propose a simple method to make more precise the screening LCA of an electronics system and show that this method significantly impacts the final results of the LCA.

## CCS Concepts

* Hardware → Printed circuit boards; Integrated circuits; Communication hardware, interfaces and storage.

## Keywords

Screening LCA, Cut-off method, Dense Electronic Devices, PCBA

## ACM Reference Format:

Benjamin Chedotel, Thibaut Marty, François Berry, and Maxime Pelcat. 2025. Methodological Challenges in Conducting a Screening Life Cycle Assessment of Dense Electronic Devices: The Case of Smartphones. In *22nd ACM International Conference on Computing Frontiers (CF Companion '25)*, May 28–30, 2025, *Cagliari, Italy*. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3706594.3727964

## 1 Introduction

Electrical and Electronic Equipmentss (EEEs) increasingly impact environment by consuming ever more electrical energy [9] and producing ever more waste [8]. Enhancing their sustainability is thus an important challenge. Life Cycle Assessment (LCA) is a standardized and widely adopted method that helps quantify environmental impacts and identify opportunities for improvement [12].

When conducting an LCA, considering every element that influences a *functional unit*, i.e. a function offered to a user, is impractical due to the time required for building the inventory of these elements and evaluating each of them, that themselves result from a very long supply chain and coordination of industries. As a consequence, impacts that are insignificant compared to the major contributors to environmental impacts shall be discarded. A *screening LCA* is a preliminary task that aims to identify the most relevant processes and components in a system, to be included in the final impact evaluation. A cut-off rule is defined, that excludes the least impacting elements whose cumulative impact remains below a specified percentage of the total. According to the European Commission

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Product Environmental Footprint (PEF) methodology, this cut-off
is set to 3% of the total impacts [24].

Screening LCAs applied to the services offered by a device are
performed by looking in a generic database, such as Ecoinvent<fnref n="1" /> or
Sphera<fnref n="2" />, for materials and components corresponding to the closest
match to a rough inventory of the device subparts. These databases
provide structured data on material and energy flows required to
produce electronic components, enabling the calculation of their
environmental impacts. They constitute valuable tools for assessing
the impact of a wide range of devices. They are however necessarily
limited in the specific devices and processes they cover.

As electronic systems become increasingly complex, with higher
component densities in smaller devices, identifying individual com-
ponents and accurately evaluating their environmental impact be-
comes more challenging. This complexity diminishes the precision
of screening LCA estimates, making them less reliable for estab-
lishing a robust LCA study framework.

We demonstrate, through the LCA of the PCBAs of two smart-
phones—an iPhone 16 and a Samsung Galaxy One Note II (see
Figure 1)—that screening LCAs relying on simple identification
methods and impact calculations based only on Ecoinvent 3.11 both
under- and over-estimate the impacts of components, jeopardiz-
ing the cut-off procedure. Thus, we propose a method to improve
screening LCA in this context and discuss how to apply it.

To the extent of our knowledge, this paper is the first to tackle the
challenges of the screening LCA applied to an electronics system.
We propose methods for the screening cradle-to-gate LCA of elec-
tronic systems, and apply them to smartphones dense PCBAs. The
use and End of Life (EoL) phases of LCA are kept out of the scope
of this paper, and we concentrate on the climate change midpoint
impact of the LCA, as evaluated in kg CO2 eq equivalent emitted
by the product (Global Warming Potential (GWP)) [12].

This study focuses on scenarios where an external company
or department assumes responsibility for the system to conduct
the LCA, necessitating a reverse engineering process that will be
detailed in the paper. We will not address cases where the method-
ology is applied during the system design phase.

The paper is organized as follows. Section 2 discusses the back-
ground and related works of the current study. Section 3 details the
concept of a screening LCA. Section 4 shows the limits of screening
LCA in our context and Section 5 details the proposed method for
enhancing this screening LCA procedure. Section 6 evaluates the
results of the enhanced screening LCA and Section 7 concludes the
study.

## 2 Background and Related Works

LCA is a standardized evaluation method (ISO 14040 and 14044)
used to conduct an environmental impact assessment of a system
across all or part of its life cycle [12].

LCA consists of four main stages. The first stage is the goal and
scope definition. The goal specifies the intended application of the
results, and the target audience. The scope establishes the system
boundaries, which define the unit processes and life cycle stages
included in the study. Ideally, all inputs and outputs throughout

<figure><img src="image_2.png" /><figcaption>Figure 1: PCBA of the Samsung Galaxy One Note II smart-phone (2012)</figcaption></figure>

the entire life cycle would be considered, but practical constraints
such as time, data availability, and resources often limit the scope.
The system scope also includes a description of its function(s),
followed by the definition of a functional unit, which provides a
quantitative description of the system functions. The reference flow
represents the amount of product and energy required to provide
the functional unit to the end user of the product.

Following goal and scope definition, Life Cycle Inventory (LCI)
consists of gathering data on energy, components, and materials em-
ployed across a product life cycle. To structure this data, databases
abstract interactions via material, energy, and emission flows be-
tween human activities (unit processes) and the environment [4].
For instance, Ecoinvent distinguishes between activities (detailed
unit processes) and markets (aggregations of activities representing
the overall supply of a product/service). Activities model specific
steps (e.g., resource extraction), while markets combine outputs
from multiple activities to reflect average supply chains. From the
LCI, the Life Cycle Impact Assessment (LCIA) evaluates the en-
vironmental impacts of these inputs, such as climate change or
resource depletion.

Finally, the interpretation phase involves analyzing the results
from LCI and LCIA to provide insights that guide decision-making
and improvements in product and company environmental perfor-
mance.

Screening LCA is an initial evaluation, performed during scope
definition, that aims at pinpointing the critical phases and aspects
of a product life cycle that have the most substantial environmental
impacts. The primary goal of screening is to identify these key
areas rather than to quantify them, ensuring they are not neglected
in a more comprehensive LCA study. This approach allows for
simplifications that streamline the detailed analysis process [11].
This paper does not propose a complete LCA but instead focuses
on this specific preparation procedure.

<footnote n="1">¹https://ecoinvent.org/</footnote>

<footnote n="2">²https://sphera.com</footnote>
The study of the environmental impacts of dense electronic devices, particularly smartphones and tablets, is a subject of increasing interest as these devices become more ubiquitous and technologically advanced. Several studies explore the challenges and methodologies associated with conducting LCAs for such complex systems. The two following works are the closest to the present study.

Clément et al. [5] conduct a comprehensive analysis of LCA studies on smartphones and tablet computers, focusing on the sources of variation in the results, particularly concerning GWP. Authors highlight that the production and use phases are the most significant contributors to the overall environmental impact. They emphasize the critical role of Integrated Circuits (ICs) in impacts, and the importance of the electricity mix used in production plants, which significantly affects GWP. The study also points out discrepancies in LCI data, particularly for ICs, which can lead to substantial variations in the results. This work underscores the need for accurate and transparent LCI data and procedure to improve the reliability of LCA studies in the electronics sector.

Jud et al. [14] address the challenges of defining functional units and system boundaries for LCAs of multifunctional electronic devices. Authors perform an illustrative LCA comparison between a smartphone and a set of dedicated devices, highlighting how different user profiles and system boundaries can affect LCA results.

## 3 Screening LCI/LCIA Method

Methods for screening LCA of electronic systems, particularly dense systems, are challenged by high concentrations of components and elements in a limited volume. These large amounts of hard-to-recognize components complicate inventory. Moreover, Printed Circuit Boards (PCBs) tend to have no silkscreen layer and no component marking, making more complex their identification. Smartphones are examples of dense systems due to the compact and complex arrangement of their hardware components. Screening methods are purely visual. More advanced identification methods, whether destructive or not, are more time costly and will be discussed in Section 6 for advanced LCA methods.

If the system is not available when performing the LCA, it is important to first find a quality tear-down image of the design system, so as to be able to identify the various components. iFixit freely provides many tear-down images (e.g., Iphone 16 tear-down [3]). We perform the LCA for both approaches: the Iphone 16 LCA is performed from a tear-down image from iFixit, while the LCA of the Samsung Galaxy One Note II smartphone is done from a phone disassembled for the study.

Looking at the PCBA shown in Figure 1 (either from the tear-down or from the owned device), we see several categories of components on the PCB: Surface Mounted Device (SMD) passive components (resistors, capacitors, inductors), ICs. Added to these are connectors and other components such as crystal oscillators.

The following of this section is structured by component type (passive components, IC, PCB, and others). For each type, we explore methods for fast component identification and examine the carbon impact assessment using Ecoinvent 3.11. We choose Ecoinvent as the reference database because it is one of the most widely used databases that combines a comprehensive coverage with an

advanced transparency, making it possible to trace impacts from technosphere activities down to ecosphere activities.

### 3.1 Passive Components

<table><caption>Table 1: Visual count of small-scale components assumed to be passive components.</caption><thead><tr><th></th><th>Galaxy One Note II</th><th>iPhone 16</th></tr></thead><tbody><tr><td>Passive Components</td><td>864</td><td>1310</td></tr></tbody></table>

*Inventory.* To inventory passive components, one starts with a visual inspection. Markings on the component can help identify them by interpreting specifications, recognizing the manufacturer's silk-screened logo, or searching for it on electronic component marketplaces (e.g., Mouser, DigiKey). However, in a screening stage, small-scale components are counted and affected roughly to either capacitors, resistors and inductance categories (one-third for each). If passive components are not cut-off by the screening method, their impacts will be precised while performing the final LCA.

The visually counted number of small-scale components, assumed to be passive, for the PCBA of the two studied smartphones is presented in Table 1. Ecoinvent computes the impacts from the passive component weight, and uses the following average weights for components: capacitor: 86 mg, resistor: 9.8 mg, inductance: 2.1 mg. Capacitors weight is strongly over-estimated, as they mix large and small capacitors, while smartphones mainly use small capacitors.

*Impact assessment.* Impacts are affected by employing the Ecoinvent activities "market for resistor, surface-mounted", "market for inductor, low value multilayer chip" and "market for capacitor, for surface-mounting". In a first approximation, the passive components category is the second largest carbon contributor (Figure 6).

### 3.2 Integrated Circuits

<table><caption>Table 2: Visual count of larger components assumed to be ICs.</caption><thead><tr><th></th><th>Galaxy One Note II</th><th>iPhone 16</th></tr></thead><tbody><tr><td>ICs</td><td>46</td><td>60</td></tr></tbody></table>

*Inventory.* ICs can also be visually counted. For the iPhone 16, iFixIt provides comments on the PCBA images, partially identifying ICs. Some ICs can be identified by their silkscreen, which provides information about the manufacturer—sometimes indicating the used type of technology—and the model, allowing us to find the datasheet.

Die size is the main parameter for IC impact assessment [18]. One can retrieve the die area by referring to available documentation or estimating it based on the package's surface area. A study, used by Ecoinvent, estimates an average of 0.078 mm² of silicon die per mm² of packaged IC [21]. As a consequence, the chip package surface is measured to derive the die surface, estimated at 7.8% of
<figure><img src="image_3.png" /><figcaption>Figure 2: Illustration of the initial and enhanced screening LCA methods</figcaption></figure>

the package surface. This assumption is far from reality for large ICs, as discussed in Section 6.3. To be more precise, we would need to measure the size of the die instead of estimating it from the package size.

The visually counted number of larger components, assumed to be ICs, for the PCBA of the two studied smartphones is presented in Table 2.

*Impact Assessment.* Ecoinvent provides three activities for ICs. One describes wafer production in mm². And the other two describes logic or memory ICs in kg including wafer and packaging ("market for integrated circuit, logic type ", and "market for integrated circuit, memory type"). The correlation between IC weight and die area is based on the aforementioned study [21], estimating a typical area of 0.018 mm²/kg of IC. However, knowing that the die has the most significant impact [1], it is more precise to assess the IC impact from the die area rather than the chip weight. Therefore, a proxy IC weight should be derived from the die surface area using this relation rather than the other way around.

## 3.3 Printed Circuit Board

*Inventory.* Several PCB technologies exist, varying in aspects such as the substrate material, the number of layers, copper thickness, and soldering method, as indicated by the occasional presence of a "Pb-free" screen-printed label on the PCB. FR-4 substrate is the most common type for smartphone motherboards [2].

*Impact assessment.* To assess the impact of PCB, ecoinventbeds data for an FR-4 6-layer SMD PCBs per m², with or without lead soldering ("market for printed wiring board, for surface mounting, Pb free surface" and "market for mounting, surface mount technology, Pb-free solder").

## 3.4 Other components

Crystal oscillators, used when a quality clock signal is needed, have no entry in Ecoinvent, and, to the best of our knowledge, no study estimates the carbon impact of these components. To ensure rigor in our LCA screening, we would need to estimate it, even approximately, to avoid overlooking potentially significant impacts. It is however excluded for now, in the absence of adequate substitutes.

Connectors can be described using Ecoinvent, either as generic connectors (USB port, serial port, parallel port) or as Peripheral Component Interconnect (PCI) connectors. There is no entry for small scale connectors. Our screening LCA employs the activity "market for electric connector, peripheral type bus".

# 4 The Cut-off Challenge of Screening LCA

From the inventory reported in Section 3, we carry out a screening LCAs on the two target smartphone PCBAs.

The Samsung Note II phone is owned and can be disassembled but the Apple iPhone 16 is not. However, for this reference screening LCAs, we are employing purely visual identification and impact measurement using the Ecoinvent 3.11 database, as if performed by LCA practitioners. The only required information is a good resolution image of both PCBA sides with a scale measurement. For the Apple iPhone 16, it is retrieved from the iFixIt website<fnref n="3" />, and for the Samsung Galaxy One Note II, we disassemble the phone and photograph it (Figure 1). We obtain the carbon impacts, according to the types of component, displayed in Figure 6.

In these two screening LCAs, a clear trend is observed with the majority of impacts relating to ICs and passive components.

<footnote n="3">³https://www.ifixit.com/Guide/iPhone+16+Chip+ID/177222</footnote>
<figure><img src="image_4.png" /><figcaption>**Figure 3:** Carbon Impact Contribution of Samsung Galaxy One Note II and iPhone 16 PCBA as evaluated with a screening LCA with Ecoinvent 3.11. Section 6 shows that passive components are over-evaluated, ICs are under-evaluated, and connectors are over-evaluated, biasing the LCA cut-off rule.</figcaption></figure>

By comparing the contributions of the 4 categories: PCB, connectors, passive components and ICs (Figure 3), we can apply a cut-off for both smartphones PCBA with a threshold at 3%. This procedure leads to eliminating PCB from the rest of the study, as PCB contribution for each smartphone is estimated less than 3% of total. Moreover, passive components are estimated to be one of the major contributors to impacts and are thus kept in the final LCA procedure. However, the final LCA of Section 6 will show that the more precise impact of these components is quite low. Obtaining the precise impact of these hundreds of components is a costly, and low efficiency operation, would reduce the interest of performing screening LCA. Thus, we would need to have dense systems oriented estimates to get a more precise screening LCA.

# 5 A Method for Enhanced Screening LCA

To establish a reliable quality cut-off criterion, enhancing the precision of screening LCA is essential to prevent over- or underestimation of components. This helps accurate orientation for advanced LCA and maximizes its reliability. A fast procedure can be adopted to improve screening.

In this section, we propose such a methodology to improve the accuracy of screening LCA while maintaining its efficiency. A summary of this enhanced screening approach is illustrated in Figure 2.

An initial step consists of estimating component weights, which can be done by comparing them with similar-sized components on electronic reseller platforms. Ecoinvent, for instance, categorizes all SMD capacitors under a single classification, assigning an average weight of 86 mg per unit—representative of relatively large Multi-layer Ceramic Capacitors (MLCCs). However, the smallest MLCCs

<figure><img src="image_5.png" /><figcaption>**Figure 4:** Updated Carbon Impact Contribution of Samsung Galaxy One Note II and iPhone 16 PCBA.</figcaption></figure>

weigh only a few milligrams, significantly altering impact assessments. By directly measuring capacitors in the Samsung device, we find that the largest one has a 1608 code size. Code sizes refer to the standardized metric naming convention for SMD components, where the first two digits (16) represent the component's length (1.6 mm) and the latter two (08) its width (0.8 mm). According to Samsung Electro-Mechanics documentation on MLCC [7], such capacitors typically weigh between 4 and 6 mg, underscoring the importance of refining weight estimations.

A similar discrepancy exists for connectors. As detailed in Section 3.4, Ecoinvent distinguishes only two categories: PCI and generic connectors, assuming a weight of 10 g per unit. However, in smartphone PCBAs, small board-to-board or board-to-Flexible Printed Circuit (FPC) connectors are commonly used, often weighing just a few tens of milligrams. For instance, the SlimStack series from Molex [16] illustrates the need for supplementing database estimates with insights from component resale platforms and system technical documentation.

For ICs die, Ecoinvent assumes a 55% process yield, based on solid waste per cm² of wafer input. Using IMEC NetZero tool [13], the carbon impact per square centimeter can be assessed for this yield, with variations depending on the technology node. Ecoinvent's assumed process corresponds to the N65 node, whereas the Exynos 4412 Quad processor in the Samsung Galaxy Note II aligns with the N32 node (between N40 and N28). In contrast, the Apple A18 in the iPhone 16 corresponds to the N3 node. These differences result in an increase in processor IC-related impacts of approximately 17% for the Galaxy Note II and about 170% for the iPhone 16.

Variability in impact estimates for PCBs is highlighted by Le Gargasson et al. [10], particularly when comparing Ecoinvent and Sphera databases. To address this, authors propose a model correlating the carbon impact with PCB area and layer count. By following
these recommendations, we determine the carbon impacts for each component type, as illustrated in Figure 4 and Figure 7.

With this refined methodology, the impact of passive components and connectors is considerably reduced, while the impact assessment of ICs is enhanced. The estimated impact of PCBs is also slightly increased.

Applying the updated carbon impact assessment to the cut-off criterion, we observe that the impact of PCBs exceeds now 3% in both enhanced screening LCA analyses. Meanwhile, the impact of connectors is now below 3%, allowing them to be cut-off.

# 6 Evaluating Performance of the Enhanced Screening LCA

In this section, we validate the improved reliability of the enhanced screening LCA compared to a final LCA. To perform the latter, we use more time consuming techniques including both destructive and non-destructive methods, and review literature to refine the accuracy of the LCA.

## 6.1 Passive Components

Certain components, such as electrolytic capacitors and diodes, have a specific polarity and a marked electrode, distinguishing them from other passive components. Additional markings may appear on the PCB silkscreen printing, using letters and numbers to indicate the component type and ID (e.g., R for resistor, C for capacitor, and L for inductor), contrary to most lower density PCBs. However, dense system may lack these PCB silkscreen printings. The type of component can also be inferred by comparing size, color, and shape against known components.

If no information can be obtained through visual inspection and the system is available, the component can first be unsoldered and its impedance measured non-destructively. If the resistance increases towards infinity, it is likely a capacitor; if it is very low and varies in the presence of a magnet, it is probably an inductor. Inductors can also be differentiated from other passive components because they contain coils, which are visible in X-ray images, as illustrated in Figure 5. This picture has been generated by observing the Samsung Galaxy Note II PCBA with COMET YXLON Cougar EVO X-Ray Inspection System by Accelonix. Loops of two inductors are clearly visible in the image. Finally, to gain further insights, particularly regarding the chemical composition, the component can be ground down, and an elemental analysis can be performed using X-ray fluorescence [15].

Once the component is identified, Ecoinvent provides impact assessment for several types of passive components. However, its technological precision is limited, often distinguishing only between Surface Mounted Device (SMD) and Through-hole technology (THT). Yet, significant differences exist, for instance, between the impacts of ceramic and tantalum capacitors [19].

Using these advanced identification methods, the number of small-scale components on the PCBA of the Samsung Galaxy Note II is presented in Table 3.

## 6.2 Printed Circuit Board

The number of PCB layers can be determined non-destructively by X-ray laminography and it is often possible to count the number

<figure><img src="image_6.png" /><figcaption>Figure 5: X-ray image of two inductors from the Samsung Galaxy One Note II PCBA.</figcaption></figure>

<table><caption>Table 3: Count of small-scale components through advanced identification methods for the Samsung Galaxy One Note II.</caption><thead><tr><td>Component</td><td>Samsung Galaxy One Note II</td></tr></thead><tbody><tr><td>Resistors</td><td>225</td></tr><tr><td>Inductors</td><td>105</td></tr><tr><td>MLCC Capacitors</td><td>477</td></tr><tr><td>Tantalum Capacitors</td><td>4</td></tr><tr><td>Diodes</td><td>22</td></tr><tr><td>LED</td><td>1</td></tr></tbody></table>

of layers in destructive cross-sectional views [23]. In our case, the observation of the Samsung Galaxy One Note II has not led to a reliable evaluation of the number of layers.

We consider a 10-layer PCB, as shown in the cross-sectional view of a smartphone PCB in [17]. We linearly rescale the impact from the Ecoinvent activity "market for printed wiring board, for surface mounting, Pb free surface" from 6 layers to 10 layers to compute this impact.

## 6.3 Integrated Circuits

Ecoinvent typically assumes that the die area occupies about 7.8% of the total chip area. However, when measuring the die area of the Samsung main System on Chip (SoC) using X-rays, as shown in Figure 8, the die area is found to be approximately 68% of the chip area.

The impact of IC production also depends on the type of technology (logic, memory, analog, Micro Electro-Mechanical System (MEMS), Radio Frequency (RF), etc.), the technology node, production yield, infrastructure, geographical location, etc. [18]. Studies have developed models for the carbon impact of various technology types such as Complementary Metal-Oxide-Semiconductor
<figure><img src="image_7.png" /><figcaption>**Figure 6:** Screening LCA results for Samsung Galaxy One Note II and Apple iPhone 16 PCBA.</figcaption></figure>

(CMOS) image sensors [22] or MEMS [6]. In this study, we update the die surface of the main SoC and employ the surface/weight ratio of Ecoinvent while scaling the technology to 32 nm as in Section 6.2, while other ICs are kept unmodified. We employ the measured 68% ratio of IC surface to both Galaxy One Note II and iPhone SoCs.

With the arrival of the Heterogeneous Integration (HI) era and 2.5D/3D technologies, the tools developed for monolithic chips are no longer efficient, and new analysis methods and impact models need to be developed [20].

## 6.4 Discussion

After carrying out the advanced LCAs, we obtain the carbon impacts, by component type, shown in Figure 9. These results are of in the same order of magnitude of previous studies [5].

Enhanced screening LCA results better align with the conclusions of the final LCA results than the default screening LCA results. Estimated impacts still vary by a factor up to 5 times between enhanced screening to final LCA. As a consequence, the enhanced screening LCA better reflects the impact of sub-components than the default screening LCA. Indeed, the carbon contribution of passive components represents 5.3% of the total impact. The enhanced screening LCA decision to keep them in final LCA aligns with the final results while screening LCA does not drastically over-estimate passive components, contrary to default screening LCA. Connectors are negligible, which also aligns with advanced screening decisions of cutting them off, by contrast to default screening LCA. As for the PCB, its final impact is 2.7% of total PCBA impact for the Galaxy One Note II. As the impact of connectors is very low, its cumulated

<figure><img src="image_8.png" /><figcaption>**Figure 7:** Enhanced Screening LCA results for Samsung Galaxy One Note II and Apple iPhone 16 PCBA.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Figure 8:** Exynos 4412 Quad processor under X-ray laminography, die framed in red and package in green.</figcaption></figure>

impact with already cut-off connectors is still under 3%, which means it could have been cut off, contrary to the conclusion of the enhanced screening LCA. However, this value is borderline and the PCB is likely to still be under-estimated in the Ecoinvent database,
<figure><img src="image_10.png" /><figcaption>Figure 9: Final LCA results for Samsung Galaxy One Note II and Apple iPhone 16 PCBA.</figcaption></figure>

given the large differences between PCB in Ecoinvent and Sphera databases [10].

By retaining an under-estimated die area for the main SoC, the enhanced screening LCA cannot substitute the final LCA. On a Galaxy One Note II, 69% of ICs impacts are concentrated in the main SoC. On an iPhone 16, 86% of ICs impacts are generated by the main SoC.

# 7 Conclusion

This paper has delved into the challenges and potential improvements in conducting screening LCA for dense electronic devices, with a particular emphasis on smartphones. The primary goal has been to assess the reliability of screening LCAs in identifying significant components for detailed analysis, especially when utilizing the Ecoinvent database. The paper has been built to be reproducible by giving the Ecoinvent activites name and our measurements.

Through case studies on the iPhone 16 and Samsung Galaxy Note II, we find that screening LCA can both over- and underestimate the environmental impacts of various components. This inaccuracy undermines the establishment of a reliable cut-off criterion, which is crucial for streamlining the LCA process.

Our analysis reveals that passive components, such as capacitors and resistors, can be strongly over-estimated in a screening LCAs due to the use of average weights that do not accurately reflect their diversity. Conversely, the impact of ICs is frequently underestimated because technology nodes evolve too fast for generic databases to adapt, and precise die sizes, not revealed by documentation, are key parameters for the analysis. Additionally, there is

a significant variability in the impact estimates of PCBs between different databases, highlighting the need for more precise and updated data.

Screening LCA remains a key tool to limit the inventory efforts to components of significant impact. To enhance the reliability of screening LCAs, it is essential to supplement database information with additional research, such as utilizing component resale platforms and system technical documentation.

By addressing these challenges, future research can establish more reliable cut-off criteria, leading to more accurate and comprehensive LCAs for dense electronic devices. This, in turn, will support better-informed decision-making and improvements in the environmental performance of electronic systems.

# Acknowledgments

This study is funded by the France2030 ESOS Project - Electronics: Sustainable, Open and Sovereign, operated by French ANR (Agence Nationale de la Recherche).

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