Bill of Attributes, Life Cycle Assessment and Materials Flows: Case Study of Laptop Computers

Transcription

1 Bill of Attributes, Life Cycle Assessment and Materials Flows: Case Study of Laptop Computers Eric Williams*, Callie Babbitt, Ramzy Kahhat, Barbara Kasulaitis Golisano Institute for Sustainability, Rochester Institute of Technology, Rochester, New York, USA Department of Engineering, Pontifical Catholic University of Peru, Lima, Peru * Corresponding Author, [email protected], Abstract Life Cycle Assessment (LCA) and Material Flows Analysis (MFA) both critically depend on the attributes of products such as content of materials and parts (Bill of Attributes = BOA). While there has been significant work to develop commercial and public databases detailing production processes, there have been no comparable efforts to characterize BOA. Two issues that could critically affect LCA and MFA results are variability in BOA within a product class and evolution of BOA over time. This study examines the above issues through a case study for laptop computers, involving disassembly, BOA characterization and analysis for seven models of different years, makers and screen size. While the limited sample size does not permit conclusive characterization of trends, initial results suggest significant variability in material content and attributes among models. The silicon wafer area associated with memory appears to have increased dramatically from Introduction The life cycle inventory for manufacturing a product combines information about a product s attributes with supply chain data on materials use and emissions in processes. Mathematically this relationship can be written (in this example for carbon dioxide) as the dot product: Life cycle carbon = BOA CO /attribute () where BOA (Bill of Attributes) is a vector of product attributes such as masses of materials, content of components and other information and CO /attribute is a vector of supply chain emissions to deliver a unit of attribute (e.g. CO emitted per kg of aluminium). BOA has been long neglected in the LCA development community. While there has been significant work to develop both commercial and public process databases, there have been no comparable efforts to characterize BOA. The implicit assumption is presumably that since the product under study is in hand and processes are out there, analysts can obtain their own BOA and the need for support is on the process side. This logic is sound in principle but fails in practice. For complex products in particular, and indeed most products in the modern economy are complex, disassembly is labor intensive and reverse engineering internal attributes such as materials content requires sophisticated equipment such as a mass spectrometer. Further, building BOA through gathering information throughout the supply chain is possible in principle but faces many of the same challenges with gathering process data, including proprietary issues. From equation () is it clear that reliable BOA data is as important as reliable process data in the carbon footprinting of products. As with process data there are important methodological questions to address for BOA. What kind of data needs to be gathered? Are there useful units of aggregations of product attributes that can streamline data gathering yet still lead to reliable footprint results? When can secondary data be used versus as opposed to collecting primary data for a product? BOA is also important for Materials Flow Analysis (MFA). MFA generally aims at estimating more aggregated flows compared to often product specific LCA. Variability of materials content within a product class and over time can significant affects MFA results. The appropriate data structures and collections methods for BOA vary by product. Some products such as detergents the BOA changes slowly while for sectors such as information technology products both BOA and process material flows can be rapidly moving targets. The techniques and labor needed to determine BOA also vary. This article aims to contribute to developing data and understanding of the BOA vector of the equation for laptop computers. The broad goals of this study are: Generate primary data on BOA through physical disassembly and x-ray analysis,

2 Characterize trends and relationships between different attributes, Explore different characterizations of product attributes (e.g. silicon wafer area versus packaging area) for use in LCA. Methods -Analytical BOA can take many different forms but in general it can be broken into subcomponent vectors: BOA = function (materials, components, assemblies) () These sub-component vectors are not independent, for example, the content of silicon chips in the component affects the quantities of gold in the materials component. To discuss each component sub-vector in more detail, the materials piece is the most conceptually simple. It can be written as: materials = (mass of constituent material, mass of constituent material, ) () where constituent materials are substances such as steel, aluminium, different types of plastics and precious metals. Some of these materials (e.g. steel) are relatively easy to separate and identify with disassembly while other materials, such as precious metals, require special processing to measure quantities or data from suppliers. Note that while [] looks simple, the situation is complicated by different grades and combinations of materials. A more disaggregated description pulling out different grades and combinations of materials could make a significant difference in the carbon footprint, for example, the purification of industrial grade materials to semiconductor production grade. Note that in the formalism one could choose to put materials processing in the materials, e.g. a separate category for purified silicon. Alternatively,silicon processing could be included in the integrated circuit piece of the BOA vector. Note that breaking down laptops into constituent materials is labor intensive and for some may require special laboratory equipment. Rather than undertake a materials analysis for each and every analysed laptop, it is clearly desirable to find regular relationships between materials contents and other product characteristics. Given that the total mass of laptops varies substantially year-to-year and model to model, one avenue to explore relationships is to express the mass vector as a total mass and set of mass fractions: mass = (total mass, mass fraction, mass fraction,.) () There are many choices for how to define bills of attribute and combine with process data. The central issue is that the two must match. For example, if the process data available for semiconductor manufacturing is in the form of carbon dioxide per area of silicon wafer area, in turn the BOM definition in components must reflect contained wafer area. The lack of disaggregated process data is a central driver of what component definitions are feasible. For example, while it would presumably be more accurate to describe integrated chip content as a list of model numbers, this would not be useful unless process data for different chip models were available. Most available process data for electronics components is highly aggregated, e.g. in the form of energy use to manufacture an aggregate mix of different types of chips, circuit boards, discrete components, etc. Until more disaggregated process data becomes available in most cases the BOA data complementing the process data will be aggregated. It is important to identify patterns and relationships that both simplify the collection of BOA data and clarify what forms of BOA data are most important for reliable LCA. From these goals two sub-questions emerge: Are there regular relationships between material and component characteristics and macro product attributes? For example, does silicon wafer content correlate with screen size of a laptop computer? Can easily visible component characteristics be mapped to ones more difficult to measure? For example, does the packaging area of a chip correlate with contained silicon wafer area? There are clearly many possible relationships between product attributes one could test. The goal of this study is begin the process of developing BOA methods through analysis of a small subset of possible relationships. With luck useful relationships will be found, but since it feeds into future work, even failure of a hypothesized relationship provides useful information. At the least this effort will help clarify how to proceed with the BOA. Laptop Disassembly We disassembled seven laptops (,., and screen sizes) manufactured in different years, details of the models processed are shown in Table. The masses of bulk materials, areas of motherboards, and characteristics of chips on motherboards (number, packaging area, and silicon wafer area) were measured. The disassembly process begins with the identification and disassembly of the laptop into major component groups. The groups were chosen based on ease of disassembly and functionality. Each component group

3 represents one assembly that performs a specific function in the laptop. Components include hard disk, DVD-CD drive, floppy disk drive, battery, motherboard, modem module, LCD display, computer chassis, and others components. Power adapters have been excluded in this disassembly process. After identification and basic disassembly, each component group was then weighed and sorted for further disassembly and detailed inventory. Once the major components were separated, each component group was then further dismantled into individual components to be sorted by material type, weighed and catalogued. The disassembly was done using hand tools, including screwdrivers, pliers and wire cutters. Each part was completely disassembled to the level where each piece comprised of a single material. Disassembly was carried out until all materials were separated or no further mechanical separation was possible. Common materials were grouped together for cataloguing if they contained a number of very small and similar parts, such as screws, wires and adhesive tape. Larger pieces were identified and weighed as a single piece. Some component groups contained more than 0 parts when disassembled. Each part was given a unique part number for the ease of recording and identification. Material types for each part were then identified by physical inspection and categorized into generalized groups. For example, all types of aluminium are considered equal. Different alloys of aluminium were not identified. A magnet was used to separate ferrous and nonferrous metals. The items classified as magnesium were identified by an Mg symbol placed by the manufacturer. It is important to note that some components were comprised of multiple material types, and physical separation could not be accomplished. When these circumstances were encountered, the ratio of material types based on weight was estimated The silicon wafer area for the motherboard and memory cards for the 8 Hewlett-Packard Elitebook 90p was measured using a Glenbrook JewelBox 70T X-Ray Inspection System and associated GTI- 000 software. The circuit board was positioned in the X-ray Inspection System and the software was calibrated for the height of the manipulator arm. An existing hole in the motherboard was used as the reference to calibrate the software. All silicon wafers on the top side of the board were measured and photographed. The board was then turned over to complete the measurements of the silicon wafers on the bottom side. Prior to completing the measurements on the bottom side of the board, the calibration was verified using the reference hole. In cases where two components were adjacent on opposite sides of the board, photographs of the circuit board were used to clarify the identity of the components. To complete the measurements for the memory card, the height of the manipulator arm was changed to accommodate the larger wafer area, and the software was recalibrated using a reference hole on the memory card. Because the size of the wafer area on the memory card required the X- ray system to be used at its lowest height and maximum zoom, a wafer on the memory card was also ground and measured to verify the measurements taken with the machine. Several measurements were also taken with another circuit board that had been ground and measured, in order to verify the accuracy of the measurements taken with the X-ray system. Table : Laptop models disassembled. All models had internal CD-ROM or CD-ROM/DVD drives. (P=Pentium) Results # 7 Year Model Dell Latitude R-Series Scrn Size. Silicon wafer area Wgt. (kg) Energy use and other environmental impacts associated with semiconductor manufacturing and associated supply chains can make significant contributions to life cycle totals for a computer []. The impacts of semiconductor manufacturing is thought to be dominated by fabrication of microcircuits on silicon wafers [] wafer area is often used in the industry as a nor- D- PPX..99 Dell Inspiron RAM (MB) Dell Inspiron 00.. Latitude D00.. HP Elite Book 90P.. 09 HP Elite Book 0P P II 00 MH Hard Drive (GB) z. P III. GHz 0 Dell Inspiron Mobile P III 0 P. GHz 0 P M.0 GHz 0 Core Duo.80 GHz 0 Core Duo 80

4 malization unit for energy and materials flows. Figure shows the measured silicon wafer contained in the, DRAM and Motherboard (MB), the latter figure exclusive of and DRAM area. The most notable trend in the rapid increases in silicon area associated with DRAM. The degree to which this rapid growth in DRAM silicon wafer imply increases environmental burdens depends on the evolution of manufacturing processes. Aggregate energy use per area of wafer fabricated has shown steady, though slow, decreases []. Figure : Trends in silicon area in Motherboard, and DRAM. Summary information on models: Silicon area (square millimeters),000,00,000,00, : 999,. screen, MB DRAM 7 Model Number Motherboard DRAM. Relationship between silicon wafer and packaging area While the use of the X-ray inspection system simplifies data collection for silicon wafer contained in microchips compared to grinding chips, it would be ideal if one could estimate the environmental impacts from externally measurable characteristics such as the area of packaging or number of pins. The hope is a reproducible relationship exists between internal and external characteristics. Figure explores the relationship between the area of contained silicon wafer area and external packaging, plotted as a function of the year of manufacture. There are observable temporal trends, roughly stable ratio for motherboard chips (in the aggregate) and a rapidly increasing ratio for memory chips. Any mapping between silicon and packaging of memory, for example, must account carefully for year of manufacture. There is also variability between models that depending on the intended accuracy of a LCA, could affect results if packaging area were used as a measure. Figure : Silicon wafer area / Chip packaging area for different classes of microchips in laptop computer :,. screen, 8 MB DRAM :, screen, MB DRAM :,. screen, MB DRAM :,. screen, MB DRAM : 8,. screen, 09 MB DRAM 7: 8,. screen, 07 MB DRAM Figure shows how silicon wafer area per megabyte of DRAM varied over the different models. Not surprisingly, there is a pattern of decreasing silicon area over time. The pattern discernable from Figures and is that decreasing feature size in semiconductor manufacturing enabled more memory to fit in same area, but growth in demand for memory in personal computing far outstripped this progress. Wafer area to memory ra o (mm / MB) Figure : Ratio of silicon wafer area to megabytes of memory in different models Ra o of silicon wafer area to packaging area Year. Bulk Materials Motherboard IC Figure shows the fractions of different bulk materials identified in the seven models. Structural materials (aluminium, steel, plastic, and magnesium) represent around half the laptop weight, the mixes of these four materials varies model by model. The two 8 models show a substitute from plastic to magnesium, but given the mix of business versus home models and manufacturers, it is not clear is this is a general temporal trend for laptops. The significant share of magnesium does imply a shift in the number and value of bulk materials. We noted that the magnesium pieces in the two 8 laptops were glued to other frame materials and thus difficult to manually recover. Memory Linear (Motherboard IC) Linear (Memory)

5 00% Others Weight share of material 90% LCD Materials 80% PWB Material 70% Ba ery Cell 0% Other Plas c 0% PC + ABS 0% Ferrous 0% Copper 0% Magnesium 0% Aluminum 0% Model Number 7 Figure : Shares of material weights in laptop computers Discussion Results are subject to the caveat that the sample of laptops analysed mixed characteristics, years and manufacturer in insufficient numbers to accurately separate the effects of different variables. We found large variations in BOA for laptop computers, the most striking of which is an order of magnitude shift in silicon content of DRAM over time. Whether or not such variability would influence qualitative conclusions from an LCA or MFA would depend on the analysis. The potential for variability, particular that driven by technological change, appears significant enough to call for inclusion of BOA in future efforts to analyse ICT products. The parameterization of attributes in order map to process data is non-trivial. Different choices to parameterize semiconductors, for example, such as chip silicon area, packaging area, mass, and pin number could change LCA results. The appropriate matching of attribute choice and process data to improve accuracy is an important issues that has yet to be explored. While this work focuses on laptop computers, the issues raised here are general. Variability of BOA for a product class can be significant, affecting the reliability of LCA and MFA. Variability and uncertainty in BOA, to our knowledge, has yet to be considered however. Literature [] L. Deng, C. Babbitt, and E. Williams, Economic-Balance Hybrid LCA Extended with Uncertainty Analysis: Case Study of Laptop Computer, Journal of Cleaner Production 9(): 98-0 (0) [] E. Williams, R. Ayres, and M. Heller, The.7 kg microchip: energy and chemical use in the production of semiconductors, Environmental Science & Technology (), 0-0, Dec. () [] L. Deng and E. Williams, Functionality versus Typical Product Measures of Energy Efficiency: Case study of Semiconductor Manufacturing, Journal of Industrial Ecology () : 08 (0)