# Migration of keywords through diffusion phases – a diachronic analysis for the identification of emergences in the research field of optoelectronic devices

Edgar L. Schiebel, Marianne Hörlesberger, Ivana Roche, Claire François, Dominique Besagni

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Edgar L. Schiebel, Marianne Hörlesberger, Ivana Roche, Claire François, Dominique Besagni. Migration of keywords through diffusion phases – a diachronic analysis for the identification of emergences in the research field of optoelectronic devices. ISSI 2009, BIREME/PAHO/WHO; Federal University of Rio de Janeiro, Jul 2009, Rio de Janeiro, Brazil. pp.194-205. hal-00550613

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# Migration of keywords through diffusion phases – a diachronic analysis for the identification of emergences in the research field of optoelectronic devices

Edgar Schiebel¹, Marianne Hörlesberger¹,

Ivana Roche², Claire François², Dominique Besagni²

¹marianne.hoerlesberger@arcs.ac.at, edgar.schiebel@arcs.ac.at

Austrian Research Centers GmbH, Tech Gate Vienna, Donau-City-Straße 1, 1220 Wien, Austria, Tel.: +43 (0) 50 550-4524, Fax: +43 (0) 50 550-4500

²ivana.roche@inist.fr, dominique.besagni@inist.fr, claire.francois@inist.fr

INIST-CNRS, 2 allée du Parc de Brabois, CS 10310, 54519 Vandoeuvre-lès-Nancy, France, Tel.: +33 (0) 383504600, Fax: +33 (0) 383504733

## Abstract

This paper focuses on the identification and characterisation of emerging topics within a technological field based on scientific literature data, namely in the field “optoelectronic devices”. The motivation for this work roots in the framework of the PROMTECH project, whose principal objective was to produce a methodology allowing the identification of promising emerging technologies. In this project, the study of the intersection of Applied Sciences as well as Life (Biological & Medical) Sciences domains and Physics with bibliometric methods produced 45 candidate technological fields and the validation by expert panels led to a final selection of ten most promising ones.

In order to detect the emerging topics, we combine two methodological approaches. The first one is a diachronic cluster analysis and the second one introduces a new modelling of field terminology evolution based on bibliometric indicators: the diffusion model.

This methodology allows us to answer the following questions: Which technological aspects within our considered field can be detected? Which of them are already established and which of them are new? How are the topics linked to each other?

## Introduction

Emerging technologies are essential to advances in research, industry and society. Early recognition of new and alternative products and procedures is a strategic necessity contributing to timely assessment and decision-making. But the detection of emerging technologies remains quite a problem which generates studies in a large range of domains from marketing to bibliometrics.

The selection tree introduced by Armstrong & Green (2007) gives a picture of the landscape of the forecasting methods that could be employed to detect these emerging technologies. It illustrates the dichotomy between judgmental and quantitative forecasting methods and shows the great diversity of existing approaches: from the Delphi or the Nominal Group Technique ones, whose forecasts are obtained in a structured way from two or more experts, to the methods combining expert domain knowledge and statistical techniques allowing the identification of causal forces acting on trends.

When the available collected data is enough to apply quantitative methods, an important question remains concerning the type of the data employed to forecasting: scientific literature databases and patent databases are the most often sources used to detect new themes by bibliometric analysis applying statistical techniques comparatively simple as growth curves
analysis or more sophisticated as clustering or network analysis (Noyons, 2004; Daim et al., 2006; Mogoutov & Kahane, 2007; Kajikawa et al., 2008). The addition of a third type of data coming from state-funded research grants is very interesting and allows, if the three sources of information are analyzed as a whole and not as separate entities, to gain an understanding of the triple-helix interfaces between university, industry and government (Salerno et al., 2006; Mogoutov et al., 2008).

The framework for this study is the PROMTECH project, financed by the European Commission, whose principal objective was to produce a methodology for the identification of promising emerging technologies. The basic hypothesis was the observation that many new emerging technologies of the last decades have drawn on the technical application of physical knowledge. The growing relevance of Physics for technology can be explained by the increased complexity of today's technology. Indeed, there are many examples showing that advanced technology in Applied Sciences as well as in Life Sciences is increasingly linked to recent outcomes of research in Physics.

In this project, the study of the intersection of Applied Sciences as well as Life (Biological & Medical) Sciences domains and Physics with bibliometric methods, validated by expert panels, led to a final selection of ten most promising technological fields.

The core idea of this contribution lies in presenting the development of methodologies for detecting new topics within one of these technological fields. We choose the field “optoelectronic devices”, because this field is one of the most promising technology fields of the last decade. Light emitting diodes gain more applications in cars and housing lighting, OLED displays are introduced in electronic devices and consumer electronics. Optimisation of the luminous power and the tuning of the light spectrum are the important research topics.

In order to identify promising emerging research topics and their diffusion stage, we use a diachronic analysis by splitting the literature data in two periods, and applying the 2 methodologies:

- the “diffusion model”, that is an extended TF IDF approach that distributes keywords in different diffusion stages in order to model the field terminology evolution (Schiebel & Hörlesberger, 2007) and

- the “diachronic cluster analysis”, using the software-tool Stan analyst® (Polanco et al., 2001).

Firstly we describe the data acquisition. Secondly the applied methodologies are described. Thirdly the results of the applied methodologies to the field “optoelectronic devices” are presented. Finally we discuss the two approaches, summarize and conclude our discussion.

## Data acquisition

The data framework is a sample recorded from the PASCAL database that was specifically adapted to the purpose of our approach, providing broad multidisciplinary coverage of scientific publications and containing, nowadays, about 17 million bibliographic records that are derived from the analysis of the scientific and technical international literature published predominantly in journals and conference proceedings. In this work, we restricted our study to a corpus of publications coming from journals only. The queries operated in this work were exclusively based on the classification categories given in the database and assigned to the individual publications, either manually by scientific experts or automatically based on a content analysis. These classification categories belong to the PASCAL classification scheme that is a taxonomy of every field and subfield of all the disciplines covered in the database. Each individual publication also benefits from a manual or automatic indexing by keywords. After a verification step, done by a scientific expert, that terminology can be employed in our analysis.
# Methodology

In this section, we introduce the two methods we employed to follow the evolution of the technological field. In previous work, we presented their detailed description (Roche et al., 2008).

Firstly, the diffusion model is used to evaluate the term status in the considered technological field, comparing them with theirs status in the other technological fields where they are present and measuring their degree of emergence in order to provide a global field characterisation.

Secondly, the diachronic cluster analysis considers the content of the technological field by a clustering approach allowing to organize the data in sub-topics and to analyse the links between them.

## *Diffusion model*

The diffusion model is based on the assumption that new findings are published in articles in a research field. Keywords that describe the research serendipities occur in the first stage in an unusual manner. In the second stage the research intensifies and established keywords are used and in a third stage the research results like natural science effects, methodologies, test arrangements, materials and applications diffuse to other research fields. In the first approach a bibliometric filter was used to assign the descriptors of articles as a whole in three diffusion stages for the period from 1996 to 2003. It gave an idea about diffusing research topics during the whole period. In this paper a time dependency is introduced. The span of time is divided into two periods. Table 1 shows this new perspective.

<table><caption>Table 1. Taxonomy of the migration pathways of research topics (descriptor terms) through different stages of the diffusion model in two time periods</caption><thead><tr><th rowspan="2">Periods</th><th colspan="3">Diffusion Model</th></tr><tr><th>Stage 1<br>Unusual Terms</th><th>Stage 2<br>Established Terms</th><th>Stage 3<br>Cross Section Terms</th></tr></thead><tbody><tr><td>1996 to 1999 (period I)</td><td>1</td><td>3</td><td>5</td></tr><tr><td>2000 to 2003 (period II)</td><td>2</td><td>4</td><td>6</td></tr></tbody></table>

Following the diffusion approach it can be assumed that keywords migrate from earlier stages in the first period to later stages in the second period like the following:

1. terms from stage 1 in the first period stay in this stage or migrate in the second period to stage 2 or stage 3

2. terms from stage 2 in the first period stay in stage 2 or migrate to stage 3

3. terms from stage 3 stay in stage 3

We defined pathways as follows:

Pathway 1: the same terms remain in stage one in both periods

Pathway 2: terms move from stage 1 in first period to stage 2 in second period

Pathway 3: terms move from stage 1 in first period to stage 3 in second period

Pathway 4: the same terms remain in stage two in both periods

Pathway 5: terms move from stage 2 in first period to stage 3 in second period

Pathway 6: the same terms remain in stage three in both periods

The terms in the different stages were identified and the migration of terms between different stages over the two time periods where traced as pathways.

This procedure has been performed on the basis of articles for the field “optoelectronic devices”. Terms were extracted from the descriptor field. Thus developments of the technology could be traced over the two time periods

We constructed a diffusion matrix for each period by assigning the terms to the three stages.

As we already published in the mentioned article we used a bibliometric filter. Table 2 shows
the filter and the values for optoelectronic devices for the first period. As the technology has to be identified within prepared 45 technology fields the field name "optoelectronic devices" is the first selection criterion. Each condition reduced the number of keywords or classified them to be assigned to a diffusion stage.

## Diachronic cluster analysis

The diachronic cluster analysis is based on the study of the evolution of cluster maps and contents obtained by a clustering method and considering data sets related to two, or more, successive time periods. In this work, after splitting the corpus in the two time periods I and II we applied a clustering algorithm on the corpus of each period, in which documents are represented by the keywords existing in the bibliographic references. The clustering tool we used (Lelu & François, 1992; Lelu, 1993) applies a non-hierarchical, non-supervised clustering algorithm, the axial K-means method, that allows to produce clusters presenting particular characteristics:

- they can overlap because the clustering method allows a document or a key-word to belong to more than one cluster;

- the constituting elements of a cluster, documents and key-words, are ranked by decreasing similarity with the cluster ideal type.

Each cluster is homogeneous subset of the information contained in the analyzed corpus, corresponding to a topic. After the clustering, we represent graphically its results employing two methods: a principal component analysis and a connected component analysis. The first one produces a global map of the topics. The topics that are very far apart represent clusters very different with respect to the key-words defining them.

The connected component analysis is then employed to show the relationships that exist between the clusters. It is a method based on the graph theory and defining the connected components representing the relative proximity between clusters. The distance between two clusters is defined as the cosine of the angle between the two axes representing the clusters. A lower and an upper thresholds are defined as respectively the minimum and the maximum value of the cosine between the clusters. Nine intermediate levels are defined in that interval. We consider that, at a given level, two clusters are connected if the value of the cosine associated to them is within the range of values of this level.

Finally, we analysed the evolution between the two cluster sets and the two maps by examining the vocabulary related to the clusters of each period using a comparison matrix (Roche et al., 2008).

## Results

The PASCAL database query delivered 3,871 articles in the field Optoelectronic Devices. In the first period we had 1,797 documents and 2,345 different keywords among which 1,527 (65%) appear less than 3 times. The second period had 2,074 articles and 2,738 terms among which 1,762 (64%) occur less than 3 times.

## *Diffusion model*

In the first period corpus, we had 2,345 different keywords. Their categorization into diffusion stages was performed by using the bibliometric filter presented in table 2.

<table><caption>Table 2. Filter to identify stages in the period 1996 to 1999</caption><thead><tr><td>Indicator</td><td>Number of Terms</td><td>Comment</td></tr></thead><tbody><tr><td>Technology Field: Optoelectronic Devices</td><td>2,345</td><td></td></tr><tr><td>Minimum Number of Articles: 3</td><td>817</td><td></td></tr><tr><td>TFIDFTech > 0,0008</td><td>289</td><td></td></tr></tbody></table>
<table><tr><td>High Diffusion: small Gini &lt; 0,95</td><td>92</td><td>Stage 3 Terms</td></tr><tr><td>Low Diffusion: large Gini &gt; 0,95</td><td>197</td><td></td></tr><tr><td>Relative Frequency &gt; 0,0035</td><td>96</td><td>Stage 2 Terms</td></tr><tr><td>Relative Frequency &lt; 0,0035</td><td>101</td><td>Stage 1 Terms</td></tr></table>

The TFIDFTech was a modified TFIDF measure. We took the relative frequency of articles as the local weight TF: the number of articles a keyword occurs in the descriptor field divided by the total number of articles in this field. The global weight was the number of technologies, where the same keyword occurs at least in one article. We used a threshold that selected 250 to 300 keywords. The first third of these keywords with a Gini index smaller than 0,95 consisted of terms that occur in many of the 45 technologies therefore these were defined as keywords that were well established and broadly diffused to other technologies. The remaining two thirds of keywords were divided in two halves: keywords with a low relative frequency were seldom used and the remaining occurred more often but mostly in the respective technology field. The results are partly shown in table 3.

<table><caption>Table 3. Keywords by diffusion stage in period 1996 to 1999 (Selection)</caption><thead><tr><td>Stage 1: Unusual Terms</td><td>Stage 2: Established Terms</td><td>Stage 3: Cross Section Terms</td></tr></thead><tbody><tr><td>Acousto-optical devices</td><td>Aerospace instrumentation</td><td>Absorption coefficients</td></tr><tr><td>Aluminium Nitrides</td><td>Aluminium arsenides</td><td>Aluminium</td></tr><tr><td>Automatic optical inspection</td><td>Aluminium compounds</td><td>Arrays</td></tr><tr><td>Binary compound</td><td>Amorphous semiconductors</td><td>Avalanche breakdown</td></tr><tr><td>Cadmium tellurides</td><td>Astronomical instruments</td><td>Bolometers</td></tr><tr><td>Cerium additions</td><td>Astronomical telescopes</td><td>Cadmium compounds</td></tr><tr><td>Chirp modulation</td><td>Avalanche photodiodes</td><td>Carrier lifetime</td></tr><tr><td>Clutter</td><td>Brightness</td><td>Carrier mobility</td></tr><tr><td>Colour displays</td><td>Carrier density</td><td>CCD image sensors</td></tr><tr><td>Commerce</td><td>Chemical interdiffusion</td><td>Charge injection</td></tr><tr><td>Conjugated polymer</td><td>Complete computer programs</td><td>Computerized simulation</td></tr><tr><td>Coordinates</td><td>Conducting polymers</td><td>Doped materials</td></tr><tr><td>Coplanar waveguides</td><td>Conduction bands</td><td>Electrical conductivity</td></tr><tr><td>DLTS</td><td>CVD</td><td>Electroluminescence</td></tr><tr><td>EBIC</td><td>CVD coatings</td><td>Electron mobility</td></tr><tr><td>Electrochemical cells</td><td>Dark conductivity</td><td>Electro-optical devices</td></tr><tr><td>Electroluminescent device</td><td>Deep energy levels</td><td>Electro-optical effects</td></tr><tr><td>Electroluminescent displays</td><td>Dislocation density</td><td>Electro-optical modulation</td></tr><tr><td>Electronic speckle pattern interferometry</td><td>Electroabsorption</td><td>Energy gap</td></tr><tr><td>Far infrared radiation</td><td>Electroluminescent devices</td><td>Epitaxy</td></tr><tr><td>Ferroelectric liquid crystals</td><td>Electron-hole recombination</td><td>Erbium</td></tr><tr><td>Goniometers</td><td>Electro-optical switches</td><td>Excitons</td></tr><tr><td>Heavily doped semiconductors</td><td>Elemental semiconductors</td><td>Experimental study</td></tr><tr><td>Heterodyne receivers</td><td>Focal planes</td><td>Fourier transform optics</td></tr><tr><td>III-VI semiconductors</td><td>Gallium arsenides</td><td>Gallium compounds</td></tr><tr><td>Image scanners</td><td>Gallium nitrides</td><td>High-speed optical techniques</td></tr><tr><td>...</td><td>...</td><td>...</td></tr></tbody></table>

In the second period corpus, that contained articles of the period 2000 to 2003, we found 2,738 keywords that were categorized into diffusion stages by using the bibliometric filter of table 4. The growth of articles from period I to period II was about 16 percent. Differences to the first period in the thresholds of the filter indicators were not really remarkable.
<table><caption>Table 4. Filter to identify stages in the period 2000 to 2003</caption><thead><tr><td>Indicator</td><td>Number of Terms</td><td>Comment</td></tr></thead><tbody><tr><td>Technology Field: Optoelectronic Devices</td><td>2,738</td><td></td></tr><tr><td>Minimum Number of Articles: 3</td><td>976</td><td></td></tr><tr><td>TFIDFTech > 0,0007</td><td>273</td><td></td></tr><tr><td>High Diffusion: small Gini < 0,94</td><td>95</td><td>Stage 3 Terms</td></tr><tr><td>Low Diffusion: large Gini > 0,94</td><td>178</td><td></td></tr><tr><td>Relative Frequency > 0,0045</td><td>82</td><td>Stage 2 Terms</td></tr><tr><td>Relative Frequency < 0,0045</td><td>96</td><td>Stage 1 Terms</td></tr></tbody></table>

In general, it can be said that the research field, Optoelectronic Devices, consisted of several research topics in the context of sourcing, detecting and controlling light. There are some very special research topics like “Superconductor-insulator-superconductor mixers”, so called SIS-mixers, that are used in space technology for the transforming of frequencies in space observation on one side of the spectrum of specialised research and LCD-displays, CCD devices for digital cameras or light emitting diodes on the other side of the spectrum of more applied research, see table 5.

<table><caption>Table 5. Keywords by diffusion stage in period 2000 to 2003 (Selection)</caption><thead><tr><td>Stage 1: Unusual Terms</td><td>Stage 2: Established Terms</td><td>Stage 3: Cross Section Terms</td></tr></thead><tbody><tr><td>Aluminium antimonides</td><td>Aerospace instrumentation</td><td>Aluminium compounds</td></tr><tr><td>Aluminium complexes</td><td>Aluminium arsenides</td><td>Amorphous semiconductors</td></tr><tr><td>Aluminium nitride</td><td>Avalanche photodiodes</td><td>Antimony compounds</td></tr><tr><td>Aspherical optics</td><td>Binary compound</td><td>Band structure</td></tr><tr><td>Astronomical observation</td><td>Cadmium tellurides</td><td>Binary compounds</td></tr><tr><td>Attitude measurement</td><td>Carrier density</td><td>Bolometers</td></tr><tr><td>Auger effect</td><td>CMOS image sensors</td><td>Brightness</td></tr><tr><td>Blackbody radiation</td><td>Dark conductivity</td><td>Cadmium compounds</td></tr><tr><td>Blue light</td><td>Dislocation density</td><td>Carrier lifetime</td></tr><tr><td>Charge coupled device</td><td>Electrical properties</td><td>Carrier mobility</td></tr><tr><td>Color filter</td><td>Electrical resistivity</td><td>CCD camera</td></tr><tr><td>Color image</td><td>Electroabsorption</td><td>CCD image sensors</td></tr><tr><td>Complementary MOS technology</td><td>Electroluminescent devices</td><td>Charge injection</td></tr><tr><td>Contact potential</td><td>Electron spin polarization</td><td>Chemical interdiffusion</td></tr><tr><td>Coplanar transmission lines</td><td>Electron-hole recombination</td><td>Chip</td></tr><tr><td>Core levels</td><td>Electro-optical devices</td><td>Computerized simulation</td></tr><tr><td>Deep level</td><td>Electro-optical modulation</td><td>Conducting polymers</td></tr><tr><td>Digital instrument</td><td>Far infrared radiation</td><td>Current density</td></tr><tr><td>Distributed Bragg reflector lasers</td><td>Focal plane arrays</td><td>Dark current</td></tr><tr><td>Electrochemical devices</td><td>Focal planes</td><td>Deep energy levels</td></tr><tr><td>Electronic countermeasures</td><td>Gallium arsenides</td><td>Doping</td></tr><tr><td>Emission</td><td>Gallium nitrides</td><td>Electrical conductivity</td></tr><tr><td>Europium Complexes</td><td>Ge-Si alloys</td><td>Electroluminescence</td></tr><tr><td>Exciton</td><td>High sensitivity</td><td>Electron traps</td></tr><tr><td>Filled polymers</td><td>Impurity states</td><td>Elemental semiconductors</td></tr><tr><td>Flip-chip devices</td><td>Indium arsenides</td><td>Energy gap</td></tr><tr><td>...</td><td>...</td><td>...</td></tr></tbody></table>

The dynamics in the research could be demonstrated by the pathways of the keywords. Pathway 1 reflected the research topics that stayed in the stage 1, as it is shown in table 6.
There are only 3 to 6 publications per keyword and the number stayed stable over the two periods. It represents research about topics like SIS-Mixers, where only a few people work worldwide, or devices like optical fiber couplers, that were used and mentioned but were not entity of intensive research.

<table><caption>Table 6. Terms of pathway 1</caption><tr><td>Indium antimonides</td><td>Porous semiconductors</td></tr><tr><td>Indium nitrides</td><td>Semiconductor laser</td></tr><tr><td>Microcavity</td><td>Spectral response</td></tr><tr><td>Narrow band gap semiconductors</td><td>Submillimeter wave antennas</td></tr><tr><td>Nonradiative transitions</td><td>Superconducting microbridges</td></tr><tr><td>Optical fiber couplers</td><td>Superconductor-insulator-superconductor mixers</td></tr><tr><td>Photoresistors</td><td></td></tr></table>

Pathway 2 in table 7 indicates growing research dynamics because terms that occurred only seldom in the first period moved to the phase of established terms in the second period. One example were cadmium telluride, what was a semiconductor material used for thin film solar cells, so called “advanced thin film” modules. Its properties fitted perfectly to the solar spectrum. There was a high potential for high-efficiency solar cell modules with a low-cost manufacturing processes. The “quantum confined Stark effect” is used to shift the light spectrum in light emitting diodes, what is important for the optical tuning. Light emitting diodes have a very high application potential in daily life and technical applications therefore together with high potential in basic research there is a growing research interest.

<table><caption>Table 7. Terms of pathway 2</caption><tr><td>Binary compound</td><td>p n heterojunctions</td></tr><tr><td>Cadmium tellurides</td><td>Photodiode</td></tr><tr><td>Far infrared radiation</td><td>Quantum confined Stark effect</td></tr><tr><td>Injection laser</td><td>Semiconductor-metal boundaries</td></tr><tr><td>LPE</td><td>Ultraviolet photoelectron spectra</td></tr><tr><td>Mercury tellurides</td><td></td></tr></table>

We found only two terms that moved from stage 1 in the first period to stage 3 in the second period. Jumping ahead from unusual and new findings to the use in many other fields is a big step. Optical tuning of light emission diodes was very essential: as LEDs became bright enough for practical use in daily life or for special technical usage the composition of the proper emitting spectrum was an important matter of research. The traditional semiconductor technology needed crystals but in blue LED technology the needed GaN had bad crystal properties therefore thin layers which ternary compounds were developed and are intensively investigated. This breakthrough can be tracked by the migration of “ternary compound” from stage 1 in period I to stage 3 in period II within only some years, see table 8.

<table><caption>Table 8. Terms of pathway 3</caption><tr><td>Optical tuning</td><td>Ternary compound</td></tr></table>

Pathway 4 reflected all the established research topics in both time periods: Gallium arsenides and Gallium nitrides as basic materials for advanced thin film LED, MOCVD that stands for Metal-Organic Chemical Vapour Deposition what is a technique for depositing thin layers of atoms onto a semiconductor wafer. A heavily growing field was the application of organic semiconductors in optoelectronics. Organic semiconductors are applied in optoelectronic devices such as organic light-emitting diodes (OLED), organic solar cells and organic field
effect transistors (OFET), they can be easily produced and optoeletronic polymers allowed the construction of flexible displays.

<table><caption>Table 9. Terms of pathway 5</caption><tr><td>Aluminium compounds</td><td>III-V semiconductors</td></tr><tr><td>Amorphous semiconductors</td><td>II-VI semiconductors</td></tr><tr><td>Brightness</td><td>Optical arrays</td></tr><tr><td>Chemical interdiffusion</td><td>Radiation quenching</td></tr><tr><td>Conducting polymers</td><td>Schottky barriers</td></tr><tr><td>Deep energy levels</td><td>Semiconductor thin films</td></tr><tr><td>Elemental semiconductors</td><td>Tunnel effect</td></tr></table>

In Pathway 5, see table 9 we found research topics that were established in the first period and migrated to applications in many other technology fields. One example is the already mentioned organic semiconductors and the thin film III-V semiconductors. Optical arrays are used in “storage and reproduction of information”, “global pollution and environment”, “radiotheraphy” etc. “semiconductor thin films” are important in “storage and reproduction of information” and “transistor technology”.

Pathway 6 comprises broad applications of optoelectronic devises and research topics of it like CCD image sensors used in digital cameras but also natural science effects like the exciton. An exciton is a bound state of an electron in an isolator or a semiconductor and an imaginary particle called an electron hole. Of course, all optoelectronic devices like LED, photodetectors, infrared detectors, photodiodes, semiconductor lasers and used materials like cadmium compounds, Gallium and Indium Compounds are part of the cross section term in stage 3.

### Diachronic cluster analysis

The diachronic cluster analysis applied on the two periods I and II, allows us to determine which topics of the second period have roots in the first one and which topics are new in the second period. The clustering algorithm produced 17 clusters involving 1,599 documents and 348 keywords in period I (P1), and 20 clusters relating 2,073 documents and 456 key-words in period II (P2). Let us notice that the very important reduction of the number of key-words associated to the clusters in both periods, around 50% with respect to the initial number of terms obtained directly from the query results, does not entail a significant loss of information at the level of the documents. Indeed we had 89% and 100% of the initial information corpora in the clusters of respectively P1 and P2.

Analysing the comparison matrix we can singularize for the second period:

- nine homonymous clusters, but only four of them seem to be stable,

- two clusters with new titles but presenting characteristics of stability,

- four clusters with low marginal values (clusters with a low inheritance from clusters of P1: are they new clusters?),

- six clusters with high marginal values (clusters with a strong inheritance from different clusters of P1).

Finally, we have detected a particular behaviour of two P1 clusters: they seem to supply at the same time and in the same way five clusters of the second period.

Having expressed these hypotheses we asked the scientific expert to validate them accordingly to both, the content of clusters, and the cluster organization in the maps. Figures 1 and 2 show the cluster maps of respectively the first and the second period. In both maps, each dot corresponds to a cluster and each line gives the connexion level between pairs of clusters. Note that we considered here only the 5 highest levels (out of 10) corresponding to the strongest connexions between clusters. In Stan analyst®, the level of connexions is code-
coloured. Since these colours do not come out in print, we mentioned the level next to the connexion.

The analysis of the expert seems to confirm the relative stability of five P2 clusters, each of them coming exclusively from an only P1 cluster that can be its homonymous or not. They are: “Electroluminescent devices”, “Elemental semiconductors”, “Infrared imaging”, “Bolometers” and “Photodetector”.

The keywords of P2 cluster “Light emitting diodes” come predominantly from its homonymous cluster in the first period, but also from others P1 clusters: “Laser diodes” and “Electroluminescent devices”.

The P1 clusters “Electroluminescent devices” and “Light emitting diodes” seem to “feed” their homonymous P2 clusters but also, and with important rates, the P2 clusters “Light emitting diodes”, “Organic semiconductors”, “Organic light emitting diodes” and “Electroluminescence”. The analysis of the cluster maps obtained for the two periods confirms this observation. Indeed, the pair of clusters “Electroluminescent devices” and “Light emitting diodes” shows a notable evolution, coming from a marginal position in the P1 cluster map (see dotted ellipse in Figure 1) to a central role in the P2 cluster network (notice dotted ellipse in Figure 2) where they find themselves linked to the clusters which they “fed”. In this network, the expert points out clusters “Organic light emitting diodes” and “Organic semiconductors” that deal with new themes coming from the Organic Electronics domain. The grouped position of these clusters on the cluster map is quite logic as well as their location in the central network that confirms the enlarging importance of these new technologies in the second period.

<figure><img src="image_2.png" /><figcaption>Figure 1. First period cluster map of “Optoelectronic devices” technological field.</figcaption></figure>

If expert analysis leads to assert that, from a macroscopic point of view, the considered field seems to be relatively stable between the two periods because it is dealing with optoelectronic devices based on mineral or organic semiconductors, a deepest look at the application level allows the expert to observe:
- in the P1 period, the applications dealing with photodetectors (imagery, measuring instruments, sensors,...) are dominant and the electroluminescent diodes are completely isolated;

- in the P2 period, the electroluminescent diodes are dominant and are located in a central position in the cluster network.

The analysis of the cluster contents consent the expert to detect, at the material level:

- the growing relevance of research dealing with organic semiconductors. This interest is justified by the flexibility of these materials allowing the production of flexible and miniaturized electronic devices (see clusters “Organic semiconductors”, “Organic light emitting diodes” in P2);

- the rise of nanotechnologies in electronics in order to achieve a greater circuit miniaturization (internal evolution of “Photodetectors” and “Gallium arsenides” clusters);

- the renewed interest in Si-Ge semiconductors alloys and type III-V semiconductors caused by the research expansion on electroluminescent diodes already observed at the applications level (internal evolution of “Elemental semiconductors” cluster).

The analysis of the terminology associated to the P2 cluster “Contact resistance” gives an additional indication about the important role of electroluminescent diodes. Indeed, in this P2 cluster, the expert observes a massive presence of some keywords weakly represented in the first period (contact resistance, ohmic contact, solid solutions and semiconductor-metal boundaries). These notions are not new but their presence in the second period shows and confirms the growing number of optoelectronic device applications on electroluminescent diodes. Indeed, the contact quality between the diode and the electrodes is a very determinant question related to the diode performance.

<figure><img src="image_3.png" /><figcaption>Figure 2. Second period cluster map of “Optoelectronic devices” technological field.</figcaption></figure>

Finally, the analysis of the terminology associated to the cluster “Infrared imaging” present in both periods, points out a very interesting trend. This cluster deals with the application of optoelectronics to the formation of images using infrared radiation, and to the processing of
the acquired images for the purposes of measurements, identification or tracking. In P1, the infrared imaging technology is associated essentially to military applications and, in P2, the expert observes, with significant frequencies, the presence of keywords related to Biology and remote sensing. This singular observation allows the expert to move forward a hypothesis of either a technological transfer process from military usages to civil ones or, at least, an extension of the infrared imaging application field.

### *Convergences between the two analyses*

The application of the diffusion model allows to select the most important terms of the field vocabulary: 288 keywords out of 2,345 in the first period and 273 keywords out of 2,738 in the second one. These selected terms are indexing the almost totality of the corpora: 1,788 documents out of 1,797 in period I and 2,069 documents out of 2,074 in period II. The clustering results obtained with these selected terms are very close to the results produced from the whole corpora. Nevertheless, we observe that the clusters are more generic with a larger overlapping between them.

The analysis of the results obtained from the two methodological approaches operated in this work reveals some quite interesting convergence points. Let us consider the terms of the six pathways presented in table 1. According to their diffusion stage, the terms of the first period do not diffuse in a homogeneous way to the second one. Only 26% of the unusual terms (26 / 100) occur in the pathways 1, 2 and 3. 54% of the cross-section terms (50 / 92) stay in their stage (pathway 6), and 60% of the established terms (58 / 96) occur in the pathways 4 and 5. The distribution of the terms present in the six pathways according to their presence in the clusters of both periods is given in Table 10. For example, in the first cell, the fraction 2/13 means that 2 terms among the 13 of the pathway 1 (Table 6) are present in at least one cluster of each period.

<table><caption>**Table 10. Distribution of the terms of the six pathways according to their presence in the clusters of both periods**</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="3">Period 2</th></tr><tr><th>Unusual</th><th>Established</th><th>Cross-section</th></tr></thead><tbody><tr><td rowspan="3">Period 1</td><td>Unusual</td><td><strong>Pathway 1: 2 / 13</strong></td><td><strong>Pathway 2: 3 / 11</strong></td><td><strong>Pathway 3: 1 / 2</strong></td></tr><tr><td>Established</td><td>-</td><td><strong>Pathway 4: 21 / 44</strong></td><td><strong>Pathway 5: 7 / 14</strong></td></tr><tr><td>Cross-section</td><td>-</td><td>-</td><td><strong>Pathway 6: 22 / 50</strong></td></tr></tbody></table>

The inheritances between the same diffusion stage are the strongest specially for established and cross-section ones.

In order to analyse the role of these terms in the thematic structuring of the technological field we can also analyse the projection of the terms of the pathways on the cluster maps of the two periods. Concerning the terms of pathways 1 and 2 we can observe the stability of their location in isolated clusters in both periods. These unusual terms correspond to fundamental new concepts not frequent enough to participate to a cluster constitution. However we can observe that one of the two terms of pathway 1 in both periods (superconducting microbridges) seems to migrate from an isolated cluster ("Superconducting microwave devices") in period P1 to a cluster ("Bolometers") located in a quite important sub-network of clusters in period P2. Conversely, the term of pathway 3 in both periods (ternary compounds) declines, going from a very strongly positioned cluster ("III-V semiconductors") in period P1 to an isolated cluster ("Ternary compounds"). The terms of the pathways 4 and 6 appear, in the two period maps, dispersed in both isolated and linked clusters with nevertheless, in the second period, a low concentration in the isolated clusters. The spread of this great number of established and cross-section terms all over the clusters is not really surprising. Indeed, this characteristic shows that these terms are representative of
all the clusters. Finally, the terms of the pathway 5 are, in both periods, concentrated in the
principal cluster network. A deeper analysis is necessary to explain this observation.

Conclusions

The approach of alternate utilisation of different bibliometric and/or informetric methods and
scientific expertise helps validating and completing the results obtained at each step of the
work as well as to get the experts personal input on the matter at hand.

For the diffusion model it can be said that the bibliometric filter to assign keywords to stages
and the splitting of the article corpus in two periods opens a view insight of the emergence of
research topics in a technology. At a first glance it is a formal taxonomy to disassemble what
is called a technology in its “atoms” of research and traces the breakthroughs if they happen.
However, it is not a prognosis neither a prediction but terms in the unusual phase show
potentials of future developments in a technology. To discuss well-founded emergences and
research potentials it is important to mirror the results with experts who are part of the science
producing system in the reflected technology.

The diachronic cluster approach allows to have a global view of the field landscape at two successive time periods. The analysis of the cluster contents and their relative position on the cluster maps supplies indications about their similarity with respect to their respective associated keywords. The observation of cluster maps allows to detect exceptional topics and interesting topic sub-sets. Let us point out nevertheless that the expert validation remains, at each step, absolutely necessary to validate and position the analysis results in the field context by giving them a scientific foundation.

The results coming from diffusion model lead to select the most important vocabulary without disturbing the cluster maps organization, and produce a new interpretation of diachronic clustering results by introducing a new term categorization that can be projected on the clusters.

**Acknowledgments:** This work was carried out thanks to a European Union funding: Project N° 15615 (NEST) - 6th Research and Development Framework Plan. The project acronym is PROMTECH, and the project full title is "Identification and Assessment of Promising and Emerging Technological Fields in Europe". The Consortium was composed by the Austrian Research Centers GmbH – ARC (Vienna, Austria), the Fraunhofer Institut für Systemtechnik und Innovationsforschung (Karlsruhe, Germany) and the Institut de l'Information Scientifique et Technique – INIST-CNRS (Nancy, France). We would also to warmly thank our colleagues from ARC and INIST-CNRS, particularly Mrs. Nathalie Vedovotto, that took part very actively to the different project steps by bringing us their scientific and documentary expertises.

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