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Structural and chemical characterization of GaSb/GaAs quantum dot structures by TEM
Published in A G Cullis, J L Hutchison, Microscopy of Semiconducting Materials 2001, 2018
H Kirmse, I Häusler, R Schneider, W Neumann, L Müller-Kirsch, D Bimberg
In addition to the diffraction-contrast investigations the cross-section samples were inspected by energy-filtered TEM in order to investigate elemental distributions of Sb and As in the QD region. The Sb-M45 ionization edge at 553 eV shows comparably low intensity, which causes a low signal-to-noise ratio (SNR). An appropriate measuring time of 60 s or longer fails due to drift problems. Therefore, a jump-ratio map is given in Fig. 6a having a more acceptable SNR. The Sb enrichment in the GaSb QD layer is verified by the bright line running across the map. In order to gain composition profiles two rectangles were defined (cf. Fig. 6a). The corresponding profiles of the Sb signal averaged parallel to the layer are given in Figs. 6b and c. Crossing the QD an increased Sb content is found over a region of 2.6 nm. The wetting layer is about 0.9 nm thick. These values are in good agreement with the complementary information of the AS-M45 elemental map which was gained using the three-window technique.
Ultrafast All Optical Parity Generator and Checker based on Quantum Dot Semiconductor Optical Amplifier
Published in IETE Journal of Research, 2023
Semiconductors of nanometers structures are called Quantum dots. The optical properties of the quantum dots can be varied precisely by changing the size of quantum dot semiconductors. This feature has enabled researchers to produce highly monochromatic lasers and LEDs. Quantum dots have 10 times faster gain recovery and has relatively less band gap energy than bulk SOA [20,21]. Self-assembled Quantum dots are produced by growing a semiconductor on a substrate with a strong lattice mismatch between the substate and active layer [16]. Due to strain produced due to lattice mismatch, the active layer turns into three-dimensional (3D) islands with a thin layer called a wetting layer as shown in Figure 1. QD-SOA model is essentially an SOA with quantum dots in the active region of SOA. The wetting layer has high bandgap energy as compared to quantum dots. Carrier transition from the wetting layer to QD excited state and to the QD ground state is depicted in Figure 1. Quantum dots are assumed to be similar and uniform. As carriers are injected into the wetting layer, due to band gap energy difference carriers quickly move to quantum dots for the recombination process. SOA gain depends on the carrier density of QD. A commonly used device for quantum dots is InAs with GaAs as a wetting layer. The rate equation for injected carriers in the wetting layer and symmetrical quantum dots is illustrated by Sun et.al [22] and the working principle of QD-SOA is the same as mentioned in Kotb et al [18] and Sun et.al [22]. The parameters used in the simulation are cited in Table 1 [22–24].
Effects of two-photon absorption on all optical logic operation based on quantum-dot semiconductor optical amplifiers
Published in Journal of Modern Optics, 2018
To furtherly investigate the effects of TPA on optical logic operations, we compare the dependence of Q factor of XOR gates on various parameters for these two cases. The calculated Q factor shows significant dependence on the injected current, initial pulse width, pulse energy and data rate as suggested in Figure 8. As we can see in Figure 8(a), by fixing the inject current at 250 mA and FWHM 1 ps, the output quality degrades when one increases the single pulse energy of the input data for both with and without TPA cases. Without the effects of TPA, the Q factor drops very fast when pulse energy is increased from 0.1 to 0.5 pJ; while with TPA taken into account, the decrease of Q factor becomes slower. This is easy to understand. As we increase the single pulse energy, the peak power of the pulses is increased. The TPA process is enhanced when the injected pulse train has a higher peak power, which will mitigate the depletion of carrier density of the active region of the device. As a result, the dropping rate of output Q factor will become lower. In Figure 8(b), Q factor increases with the increase of the injected current until the current reaches a certain level (~275 mA with TPA and 350 mA without TPA) and then it saturates. With TPA, it saturates at a smaller current level compared with that without TPA. The injected current produces carriers to the wetting layer; thus, in each energy level in the quantum dot the carrier density is able to recover to its initial state. The more carriers produced, the faster the recovery time will become. This can reduce the pattern effects due to long recovery time of the logic operation. The TPA process also contributes to the carrier recovery, and thus the Q factor can saturate at a lower current level than that without TPA. The Q factor decreases with the increase of pulse width and data rate for both cases. Typically, digital transmission systems require the BER to be lower than 10−9, which means the Q factor should remain above the critical limit of six. This can be easily realized by reducing the input pulse width. We calculate that with TPA included, when the input pulse width is reduced to 0.9 ps with the single pulse energy unchanged, the output Q factor reaches 6.4. As indicated in Figure 8(c), as long as the injected pulse width is below 0.9 ps, this scheme is capable of generating results with low error rate (Q > 6) at the speed of 320 Gb/s. The overall Q factor of the TPA case is always higher than that without TPA. This demonstrates that TPA plays an important role in QD gain bleaching and reducing the pattern effects for ultrafast optical logic operation.