Posts Tagged ‘computers’

The Future of Speech-Based Human-Computer Interaction

Reporter: Ethan Coomber, Research Assistant III

2021 LPBI Summer Internship in Data Science and Podcast Library Development
This article reports on a research conducted by the Tokyo Institute of Technology, published on 9 June 2021.

As technology continues to advance, the human-computer relationship develops alongside with it. As researchers and developers find new ways to improve a computer’s ability to recognize the distinct pitches that compose a human’s voice, the potential of technology begins to push back what people previously thought was possible. This constant improvement in technology has allowed us to identify new potential challenges in voice-based technological interaction.

When humans interact with one another, we do not convey our message with only our voices. There are a multitude of complexities to our emotional states and personality that cannot be obtained simply through the sound coming out of our mouths. Aspects of our communication such as rhythm, tone, and pitch are essential in our understanding of one another. This presents a challenge to artificial intelligence as technology is not able to pick up on these cues.


In the modern day, our interactions with voice-based devices and services continue to increase. In this light, researchers at Tokyo Institute of Technology and RIKEN, Japan, have performed a meta-synthesis to understand how we perceive and interact with the voice (and the body) of various machines. Their findings have generated insights into human preferences, and can be used by engineers and designers to develop future vocal technologies.

– Kate Seaborn

While it will always be difficult for technology to perfectly replicate a human interaction, the inclusion of filler terms such as “I mean…”, “um” and “like…” have been shown to improve human’s interaction and comfort when communicating with technology. Humans prefer communicating with agents that match their personality and overall communication style. The illusion of making the artificial intelligence appear human has a dramatic affect on the overall comfort of the person interacting with the technology. Several factors that have been proven to improve communication are when the artificial intelligence comes across as happy or empathetic with a higher pitched voice.

Using machine learning, computers are able to recognize patterns within human speech rather than requiring programming for specific patterns. This allows for the technology to adapt to human tendencies as they continue to see them. Over time, humans develop nuances in the way they speak and communicate which frequently results in a tendency to shorten certain words. One of the more common examples is the expression “I don’t know”. This expression is frequently reduced to the phrase “dunno”. Using machine learning, computers would be able to recognize this pattern and realize what the human’s intention is.

With advances in technology and the development of voice assistance in our lives, we are expanding our interactions to include computer interfaces and environments. While there are still many advances that need to be made in order to achieve the desirable level of communication, developers have identified the necessary steps to achieve the desirable human-computer interaction.


Tokyo Institute of Technology. “The role of computer voice in the future of speech-based human-computer interaction.” ScienceDaily. ScienceDaily, 9 June 2021.

Rev. “Speech Recognition Trends to Watch in 2021 and Beyond: Responsible AI.” Rev, 2 June 2021, http://www.rev.com/blog/artificial-intelligence-machine-learning-speech-recognition.

“The Role of Computer Voice in the Future of Speech-Based Human-Computer Interaction.” EurekAlert!, 1 June 2021, http://www.eurekalert.org/pub_releases/2021-06/tiot-tro060121.php.

Other related articles published in this Open Access Online Scientific Journal include the Following:

Deep Medicine: How Artificial Intelligence Can Make Health Care Human Again
Reporter: Aviva Lev-Ari, PhD, RN

Supporting the elderly: A caring robot with ‘emotions’ and memory
Reporter: Aviva Lev-Ari, PhD, RN

Developing Deep Learning Models (DL) for Classifying Emotions through Brainwaves
Reporter: Abhisar Anand, Research Assistant I

Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis
Reporter: Aviva Lev-Ari, PhD, RN

The Human Genome Project
Reporter: Larry H Bernstein, MD, FCAP, Curator

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Laser Technology

Larry H. Bernstein, MD, FCAP, Curator



Laser Focus World   www.laserfocusworld.com

Ultrafast lasers simplify fabrication of 3D hydrogel tissue scaffolds

Multimode holographic waveguides tackle in vivo biological imaging

Mid-infrared Lasers CMOS silicon-on-sapphire process produces broad mid-IR supercontinuum

Looking Back/Looking Forward: Positioning equipment—the challenge of building a solid foundation for optics
Stability and precision have been crucial for optics since the 19th century.
Jeff Hecht

Monolithic DFB QCL array aims at handheld IR spectral analysis
Many QCLs combined on a single chip demonstrate fully electronic wavelength tuning for stand-off IR spectroscopy of explosives and other materials.
Mark F. Witinski, Romain Blanchard, Christian Pfluegl, Laurent Diehl, Biao Li, Benjamin Pancy, Daryoosh Vakhshoori, and Federico Capasso

Quantum dots and silicon photonics combine in broadband tunable laser
A new wavelength-tunable laser diode combines quantum-dot technology and silicon photonics with large optical gains around the 1310 nm telecom window.
Tomohiro Kita and Naokatsu Yamamoto

Computer modeling boosts laser device development
A full quantitative understanding of laser devices is boosted by computer modeling, which is not only essential for efficient development processes, but also for identifying the causes of unexpected behavior.
Rüdiger Paschotta



Monolithic DFB QCL array aims at handheld IR spectral analysis

Advances in infrared (IR) laser sources, optics, and detectors promise major new advances in areas of chemical analysis such as trace-gas monitoring, IR microscopy, industrial safety, and security.

One key type of photonic device that has yet to reach its full potential is a truly portable noncontact (standoff), chemically versatile analyzer for fast Fourier-transform infrared (FTIR)quality spectral examination of nearly any condensed-phase material. The unique challenges of standoff IR spectroscopy actually extend beyond advances in IR hardware, requiring the proper combination of several areas of expertise: cutting-edge optical design and laser fabrication, integrated laser electronics, thermally efficient hermetic packaging, statistical signal processing methods, and deep chemical knowledge.

At the core of the approach we have taken at Pendar Technologies is the monolithic distributed feedback (DFB) quantum-cascade laser (QCL) array. Invented in Federico Capasso’s group at Harvard University (Cambridge, MA) and licensed exclusively to Pendar, the continuously wavelength-tunable QCL array source is a highly stable broadband source that can be used for illumination in reflectance spectroscopy. Each element of the array is individually addressable and emits at a different wavelength by design.

The advantages of these QCL arrays over external-cavity (EC) QCLs stem from (1) the monolithic structure of QCL arrays and (2) their fully electronic wavelength tuning— that is, no moving gratings, allowing for much-higher-speed acquisition through improved amplitude and wavelength stability. When integrated into a system, the result is robust, stable, and field-deployable.

One of the key advances that has enabled this technology to be fielded is the high-yield fabrication of each laser ridge in the QCL array from a single wafer such that every channel simultaneously meets the specified wavelength, power, and single-mode suppression ratio. Each of these parameters is critical to both efficient beam combining and to obtaining high-quality molecular spectroscopy once integrated.

With these hurdles largely overcome, the payoff in terms of spectrometer performance lies largely in a demonstrated shot-to-shot amplitude stability in pulsed mode of <0.1%—a factor of 50 more stable than is typical for EC QCLs, even when used in the lab. Most importantly, the DFB QCL noise is random, and averages toward an Allan variance limit quickly such that detector-noise-limited, high-quality spectra can be obtained for trace levels (for example, 1–50 µg/cm2) of typical powders in just 100 ms.

More DFB array advantages While the stability advantage of DFBs vs. EC configurations has been well established, there are a few less-obvious aspects to DFB arrays that make them more suitable to real-world spectroscopy tools and, in particular, portable spectroscopy tools. For one, the laser array as a whole can maintain a 100% duty cycle while each laser in the array requires operation only over a 100/n (%) duty cycle, where n is the number of lasers in the array. Put another way, a laser array consisting of only pulsed QCLs can operate as a truly continuous-wave (CW) system, allowing for high-measurement duty cycle while possibly reducing the cost of fabrication.

In a related way, generating light for an array that has a 100% aggregate duty cycle (by using, for instance, 32 lasers at 3% duty cycle), the thermal heat-sinking requirements of the source are dramatically reduced. Indeed, our packaged prototypes do not even require active cooling to keep the system cool enough to run. A thermoelectric cooler is built into the package only to stabilize the temperature, which therefore stabilizes the 32 wavelengths (see Fig. 1).
FIGURE 1. A 200 cm-1 prototype QCL array with 32 QCLs is shown prior to beam combining and packaging (a), and experimental spectra from 32 adjacent QCLs are seen (b). (Courtesy of Pendar Technologies)
Finally, the arbitrary programmability of the QCL array opens up many new possibilities for experimental optimization. Certain lasers can be skipped, multiple lasers can fire at once, repetition rates and pulse durations can be set for each element, and so on. These advantages are only truly realized when the QCL array is instrumented into a full system.

Looking holistically at how best to integrate this new capability into a full system, it is critical to draft the link equations that govern the use of electrons to produce photons, the collection of photons scattered back, and finally the conversion from raw spectral information to chemical identification. In the case of mid-IR material identification, it becomes clear that three aspects are particularly consequential: (1) How broad a wavelength range is needed for the tool to be of maximum specificity without producing redundant or useless chemical information (that is, how many laser channels should be used, how should they be spaced with respect to one another, and over what total wavelength regime should they be spaced); (2) the mechanical and electro-optical design of the instrument; and (3) how to get the highest performance regressions against reference spectra while maintaining the high-speed identification that the QCL array actually enables.

With regard to the wavelength regions of interest (see Fig. 2), most of the spectral richness of an IR spectrum is centered in two bands, generally referred to as the functional group region (about 3.3–5.5 µm) and the fingerprint region (about 7–11 µm). The first is typically dominated by the stretch modes of certain common bond groups, while the latter includes bending modes of some functional groups as well as lower frequency modes that are characteristic of the macromolecule “backbone”—for instance, the torsional modes of a toluene ring found in many highly energetic materials. With support from the Department of Homeland Security (DHS)’s Widely Tunable Infrared Source (WTIRS) program and from the Army Research Lab, Pendar is developing a compact array module that fully covers 7–11 µm (900–1430 cm-1).

FIGURE 2. An assemblage of IR spectra of many common explosives shows that each has at least one unique absorption feature in the wavelength ranges selected. The blue shaded box indicates strong water interference in the troposphere. The figure intentionally spans beyond 1800 cm-1 so as to illustrate that no new information is gained for this chemical class by shifting the longwave-IR (LWIR) source further to the blue until the midwave-IR (MWIR) is reached.


System architecture drivers To maximize signal-to-noise (SNR) while minimizing the required acquisition time, the system architecture is driven by the following first-order considerations: 1. Increasing the laser power enabled by relaxed thermal constraints as the heat load is distributed over several modules (arrays) and laser waveguides. 2. Maximization of the measurement duty cycle enabled by the fast purely electronic control of the array, allowing close to zero-delay switching between lasers— that is, a laser is on at any time. This is also enabled by the distributed heat load among the laser units. 3. Improved source stability, wavelength accuracy, pulse-to-pulse amplitude, and frequency repeatability—all of which are needed to ensure that the source noise is not the limiting form of noise (compared to detector or speckle noise). Other researchers have studied the source-noise problem of commercial EC QCLs as well and concluded that the order-of-magnitude advantage in minimum detectable absorbance (MDA) offered by a DFB QCL carries through the full experiment.

Finally, once the spectra are digitized, the system must use complex chemometrics algorithms to ensure confident identification of threats in the presence of chemical clutter, deliberate interferents, and unknown backgrounds, without the intervention of an expert user. Our approach to real-time chemometrics is centered on the fact that for chemically cluttered situations, spectral libraries alone—no matter how large—cannot constitute the sole basis for chemometric analysis. Microphysics modeling and experimentation are also required, particularly in regard to crystal size distribution, clutter interactions, and chemical photolysis/reactions.

The key advance lies in the incorporation of chemical and physical understanding of the targets and their co-indicators. We are currently developing a four-tiered approach to the spectroscopic algorithms challenge:

1. Physics-based models. Reliable chemical detection from standoff measurements will involve transformation of the chemical signatures in the reference spectral library to reflect the physical and environmental conditions of the experiment. A physics-based model will thus be included in the detection algorithm to help us model the variability in a reference spectrum as a function of effects such as vapor pressure, deliquescence, photochemical lifetime, reactive lifetime, decomposition products, and so on to facilitate better comparison with the measured spectrum.

2. Situational effects. Effects of different substrates and their properties on the chemical signatures and the angular dependence of spectra that are not clearly linked to equations of physics and chemistry will be experimentally evaluated and included in the detection algorithm. In particular, experimentally measuring such variability will help us algorithmically model the variability of chemical signatures from some “gold standard” reference signature, which—in
addition to the physical model—will enable better detection strategies.

3. Feature-based classification. Extraction of relevant feature vectors from the reference library spectra and the knowledge of the chemistry to form a hierarchical decision tree that will help us provide different levels of classification based on the customer requirements. For instance, if a customer is only interested in finding out whether a given chemical is an explosive, then we might save on computational cost by avoiding searching through the leaves of the decision tree to find out the exact chemical.

4. Real-time atmospheric measurements. Once validated, the model will be suitable for field implementation by the inclusion of an integrated sensor suite that simultaneously records atmospheric pressure, temperature, relative humidity, solar flux, wind magnitude, and water-vapor mixing ratio. With these design drivers considered, Pendar recently completed the build of a handheld demonstration system.

Figure 3 shows the experimentally obtained spectra for two nonhazardous chemical targets as a function of stand-off distance. The yellow line in each panel shows the library FTIR (“true”) spectrum for each. Agreements of r2 > 0.9 were typical. With the prototype system as an extrapolation point, continued, focused advances in the technology are now underway to open myriad frontiers in molecular spectroscopy.


FIGURE 3. Standoff spectra of of acetaminophen and ibuprofen for three target distances. The black line shows the FTIR of the same using a diffuse reflectance accessory. The only data processing shown is the normalization of the curve areas to a common value.


ACKNOWLEDGEMENT Pendar Technologies was formed in August 2015 through a merger between Pendar Medical (Cambridge, MA), a portable spectroscopy company founded by Daryoosh Vakhshoori (who was previously at Ahura Scientific and CoreTek), and QCL sensing startup Eos Photonics (Cambridge, MA), a Harvard spinoff founded by professor Federico Capasso and his postdocs.


Quantum dots and silicon photonics combine in broadband tunable laser

A new wavelength-tunable laser diode combines quantum-dot (QD) technology and silicon photonics with large optical gains around the 1310 nm telecom window and is amenable to integration of other passive and active components towards a truly integrated photonic platform.

A new heterogeneous wavelength-tunable laser diode, configured using quantum dot (QD) and silicon photonics technology, leverages large optical gains in the 1000–1300 nm wavelength region using a scalable platform for highly integrated photonics devices. A cooperative research effort between Tohoku University (Sendai, Japan) and the National Institution of Information and Communication Technology (NICT; Tokyo, Japan) has resulted in the demonstration of broadband tuning of 44 nm around a 1230 nm center wavelength with an ultrasmall device footprint, with many more configurations with various performance metrics possible.

Recently developed high-capacity optical transmission systems use wavelength-division multiplexing (WDM) systems with dense frequency channels. Because the frequency channels in the conventional band (C-band) at 1530–1565 nm are overcrowded, the frequency utilization efficiency of such WDM systems becomes saturated. However, extensive and unexploited frequency resources are buried in the near-infrared (NIR) wavelength regions such as the thousand (T) and original (O) bands between 1000 and 1260 nm and 1260 and 1350 nm, respectively. Quantum dot-based optical gain media have various attractive characteristics, including ultrabroad optical gain bandwidths, high-temperature device stability, and small line width enhancement factors, as well as silicon photonic wire waveguides based on silicon-on-insulator (SOI) structures that are easily amenable to constructing highly integrated photonics devices.1-4

Quantum dot-based optical gain media have various attractive characteristics, including ultrabroad optical gain bandwidths, high-temperature device stability, and small linewidth enhancement factors, as well as silicon photonic wire waveguides based on silicon-on-insulator (SOI) structures that are easily amenable to constructing highly integrated photonics devices.1-4

The photonic devices used for shortrange data transmission are required to have a small footprint and low power consumption. Therefore, compact, low-power wavelength-tunable laser diodes are key devices for use in higher-capacity data transmission systems that have been designed to use these undeveloped frequency bands, and our heterogeneous tunable wavelength laser diode consisting of a QD optical gain medium and a silicon photonics external cavity is a promising candidate.5

Quantum dot optical amplifier Ultrabroadband optical gain media spanning the T- and O-band are effectively fabricated by using QD growth techniques on large-diameter gallium-arsenide (GaAs) substrates. Our sandwiched sub-nano-separator (SSNS) growth technique is a simple and efficient method for obtaining high-quality QDs (see Fig. 1).


FIGURE 1. A cross-section (a) shows a quantum dot (QD) device grown using the SSNS technique, resulting in a high-density, highquality QD structure (b) that is used to create a typical SOA (c) using QD optical gain.


In the SSNS method, three monolayers (each around 0.85 nm thick) of GaAs thin film are grown in an indium GaAs (InGaAs) quantum well (QW) under the QDs. We had previously observed many large, coalescent dots that could induce crystal defects in QD devices using a conventional growth technique without SSNS. Now, we can obtain high-density (8.2 × 1010 cm-2), high-quality QD structures since the SSNS technique successfully suppresses the formation of coalescent dots.

For single-mode transmission, a ridgetype semiconductor waveguide was fabricated for single-mode transmission. The cross-section of the semiconductor optical amplifier (SOA) has an anti-reflection (AR) coating facet to connect a silicon photonics chip with low reflection and a cleaved facet used as a reflecting mirror in the laser cavity.

To fabricate the SOA, the SSNS growth technique was combined with molecular beam epitaxy. Quantum dots comprised of indium arsenide (InAs) with 20–30 nm diameters were grown within an InGaAs QW. Seven of these QD layers are stacked to achieve broadband optical gain. Subsequently, this QD-SOA is used as an optical gain medium for the heterogeneous laser, which can be complemented by other communication technology devices such as a high-speed modulator, a two-mode laser, and a photoreceiver.6, 7

Silicon photonics ring resonator filter With the QD-SOA fabricated, a wavelength filter is fabricated next using silicon photonics techniques. It includes a spot-size converter that has a silicon oxide (SiOx) core and a tapered Si waveguide that connects the QD-SOA to the Si photonic wire waveguide while minimizing optical reflections and coupling losses (see Fig. 2).


FIGURE 2. A microscope image (a) shows a silicon-photonicsbased wavelength-tunable filter. In a transmittance analysis (b), the red and blue dotted lines indicate the transmittance of a small ring resonator with free spectral range FSR1 and a large ring resonator with FSR2, respectively, and the solid line indicates the product of each transmittance. The tuning wavelength range is determined from the FSR difference of the two rings. A smaller difference in the FSR provides a wider wavelength tuning range, even when the transmittance difference between the main and side peaks is small.


The wavelength-tunable filter consists of two ring resonators of different size. The Vernier effect of these two ring resonators allows only light of a specific wavelength to reflect to the QD-SOA. Furthermore, Tantalum micro-heaters formed above the resonators provide a means whereby the laser wavelength can be tuned through application of the thermooptic effect.

Essentially, the wavelength tuning operation of the double ring resonator wavelength filter is achieved through Vernier effects wherein a ring resonator acts as a wavelength filter with constant wavelength interval called the free spectral range (FSR), which is inversely proportional to the circumference of the ring. The tuning wavelength range is determined from the FSR difference of the two rings with FSR1 and FSR2.

A smaller difference in the FSR provides a wider wavelength tuning range, even when the transmittance difference between the main and side peaks is small. On the other hand, a sufficiently large transmittance difference is required to achieve stable single-mode lasing and is obtained using large FSR ring resonators.

Silicon photonics allows us to fabricate an ultrasmall ring resonator with large FSR because of the strong light confinement in the waveguide. The ring resonator consists of four circle quadrants and four straight lines and the radius of the circle was chosen to be 10 µm to avoid bending losses. The FSRs of the ring resonators and the coupling efficiency between the bus-waveguide and the ring resonator are optimized to obtain wide wavelength tuning range and sufficient transmittance difference.

The FSRs and the coupling efficiencies of the double ring resonators are designed to obtain a 50 nm wavelength tuning range and 1 dB transmittance difference. We have since fabricated various wavelength-tunable laser diodes, including a broadband tunable laser diode, a narrow spectral-linewidth tunable laser diode, and a high-power integrated tunable laser diode by using a silicon photonics wavelength filter and a commercially available C-band SOA.8, 9

The tunable laser diode Using stepper motor controllers, the QD-SOA—kept at approximately 25°C using a thermoelectric cooler—and the silicon photonics wavelength filter are butt-jointed (see Fig. 3). The lasing wavelength is controlled by the temperature of a micro-heater placed on the ring resonators. With physical footprints of 600 µm × 1 mm and 1 × 2 mm for the wavelength filter and the QD-SOA, respectively, the total device size of the tunable laser diode is just 1 × 3 mm.


FIGURE 3. A schematic shows how the heterogeneous wavelengthtunable laser diode is constructed.


Measured using a lensed fiber, the laser output from the cleaved facet of the QD-SOA shows single-mode lasing characteristics with a laser oscillation threshold current of 230 mA. Maximum fiber-coupled output power is 0.4 mW when the QD-SOA injection current is 500 mA. As the ring resonator temperature is increased by a heater with 2.1 mW/nm power consumption, the superimposed lasing spectra show a 44 nm wavelength tuning range with more than a 37 dB side-mode-suppression ratio between the ring resonator’s modes. The 44 nm wavelength tuning range of our heterogeneous QD/Si photonics wavelength-tunable laser is, to our knowledge, the broadest achieved to date. The 44 nm tuning range around 1230 nm corresponds to 8.8 THz in the frequency domain, which is far larger than the 4.4 THz frequency that is available within the C-band.

Our heterogeneous laser is suitable for use as a light source on a silicon photonics platform that includes other optical components such as high-speed modulators and germanium (Ge)-based detectors. In addition to application as a single-chip broadband optical transceiver for telecommunications, the laser could also be applied to biomedical imaging applications such as optical coherence tomography (OCT), considering the low absorption of NIR light at 1310 nm in the presence of water.

ACKNOWLEDGEMENTS This research was partially supported by the Strategic Information and Communications R&D Promotion Program (SCOPE), of Japan’s Ministry of Internal Affairs and Communications and a Grant-in-Aid for Scientific Research of the Japan Society for the Promotion of Science.


1. Y. Arakawam and H. Sakaki, Appl. Phys. Lett., 40, 11, 939–941 (1982).

2. D. L. Huffaker et al., Appl. Phys. Lett., 73, 18, 2564–2566 (1998).

3. R. A. Soref, Proc. IEEE, 81, 12, 1687–1706 (1993).

4. B. Jalai and S. Fathpour, J. Lightwave Technol., 24, 12, 4600–4615 (2006).

5. T. Kita et al., Appl. Phys. Express, 8, 6, 062701 (2015).

6. N. Yamamoto et al., Jpn. J. Appl. Phys., 51, 2S, 02BG08 (2012).

7. N. Yamamoto et al., Proc. OFC, Los Angeles, CA, paper W2A.24 (Mar. 2015).

8. T. Kita et al., Appl. Phys. Lett., 106, 11, 111104 (2015).

9. N. Kobayashi et al., J. Lightwave Technol., 33, 6, 1241–1246 (2015).


Computer modeling boosts laser device development

A full quantitative understanding of laser devices is boosted by computer modeling, which is not only essential for efficient development processes, but also for identifying the causes of unexpected behavior.

Computer modeling can give valuable insight into the function of laser devices. It can even reveal internal details that could not be observed in experiments, and thus allows one to develop a comprehensive understanding from which laser development can enormously profit. For example, the performance potentials of certain technologies can be fully exploited and time-consuming and expensive iterations in the development process can be avoided. Some typical examples clarify the benefits of computer modeling for improved laser device development.

Example 1: Q-switched lasers

FIGURE 1. Evolution of the transverse beam profile (shown with a color scale) and the optical power (black circles, in arbitrary units) in an actively Q-switched laser is simulated with RP Fiber Power software using numerical beam propagation. The color scale is normalized for each round trip according to the timedependent optical power so that the variation of the beam diameter can be seen.

Example 2: Mode-locked lasers

Example 3: Ultrashortpulse fiber amplifiers

FIGURE 2. The evolution of pulse energy and forward ASE powers in a four-stage fiber amplifier system with various types of ASE suppression between the stages, calculated with a comprehensive computer model

FIGURE 3. Form-based software can be used to model laser devices such as a fiber amplifier. It is essential that such forms be made or modified by the user or by technical support, so that they can be tailored to specific applications.

…. more

Documentation and support For any modeling task, documentation of methods and results is essential. The documentation must not only explain details of the user interface, but must inform the user what kind of physical model was used, what simplifying assumptions were made, and what limitations need to be considered. Unfortunately, software documentation is often neglected. In case of doubt, competent technical support should be available—not only for helping with the handling of the software, but also offering detailed technical and scientific advice. For example, a beginner may find it difficult to decide which kind of model should be implemented for a certain purpose and which possibly disturbing effects need to be considered. Such support should come from a competent expert in the field rather than just a programmer.
Rüdiger Paschotta is founder and executive of RP Photonics Consulting, Bad Dürrheim, Germany; e-mail: paschotta@rp-photonics.com; www.rp-photonics.com




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