The work is supported by the Engineering and Physical Sciences Research Council of the UK (EPSRC) Grant number EP/W032643/1.

1. Setting up Dwave Ocean Environment
<ol>
	<li>Download and install the ocean tools from the link: https://docs.ocean.dwavesys.com/en/stable/overview/install.html
	<ol>
		<li>At the terminal, type python -m venv ocean.</li>
		<li>At the terminal, type . ocean/bin/activate, as shown in Figure 1.</li>
		<li>At the terminal, type git clone https://github.com/dwavesystems/dwave-ocean-sdk.git 
		Next, type&#160;cd dwave-ocean-sdk, as shown in Figure 2. 
		Then, type python setup.py install</li>
	</ol>
	</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64930/64930fig01.jpg" /> 
Figure 1: Ocean virtual environment activation. Ocean package, as D-wave API integrated, provides a cloudy user experience over the user&#39;s own computer to the D-wave machine premise. <a href="https://www.jove.com/files/ftp_upload/64930/64930fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/64930/64930fig02.jpg" /> 
Figure 2: Ocean SDK installation. Ocean package provides necessary tool kits for developers, including a handy Cplex installation. <a href="https://www.jove.com/files/ftp_upload/64930/64930fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Installation of Cplex Python API interface
<ol>
	<li>Download and install Cplex: https://pypi.org/project/cplex/
	<ol>
		<li>At the terminal, type pip install cplex.</li>
	</ol>
	</li>
</ol>
3. Experiment configuration parameters
<ol>
	<li>Set up the experiment configuration parameters mentioned in Table 1 using Python programming notation in the script, as shown in Supplementary Figure 1. Once the script is run and executed, the underlying language will process to store the variables in RAM. A screenshot of the Python codes where the parameters are assigned values respectively is attached (Supplementary Figure 1).</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>d0</td>
			<td>87.7085 m</td>
		</tr>
		<tr>
			<td>E</td>
			<td>50 * 1 x 10-09 joules</td>
		</tr>
		<tr>
			<td>epson_fs</td>
			<td>1 * 10-12* 10 joules</td>
		</tr>
		<tr>
			<td>epson_mp</td>
			<td>0.0013 * 1 * 10-12 joules</td>
		</tr>
		<tr>
			<td>packet size</td>
			<td>4000 bits</td>
		</tr>
	</tbody>
</table>
Table 1: Energy model parameter and packet size settings.
Supplementary Figure 1: Script1. Script to set up the experiment parameters. <a href="https://www.jove.com/files/ftp_upload/64930/Supplementary Figure 1.pdf" target="_blank">Please click here to download this File.</a>
4. Python scripts
<ol>
	<li>Prepare the Python scripts to generate 198 sensor node 2D positions that are equally scattered into six sectors that divide the circular area with a radius of 50 m. 
	NOTE: The circular graph is segmented into 6 sectors. In each sector, the position of each node is treated with two corresponding variables. One is the angle, and the other is the radius. Assign values to both angle and radius using a uniform random distribution generator. The detailed procedure is shown in Supplementary Figure 2 and Supplementary Figure 3.</li>
	<li>Within each sector, ensure that the 33 sensor nodes are scattered randomly by a normal distribution. Save the 2D positions into text files by each sector under the name spelling rule as &#39;posdata&#39;+sector_no+&#39;.txt&#39; (Figure 3 and Figure 4).
	<ol>
		<li>Segment the circular area with a radius of 50 m into six sectors. The starting angular values for these six sectors make the vector A= [60,120,180,240,300,360].
		<ol>
			<li>Suppose the sector index is i, set the pole length for jth sensor node as l_{i,j}=50*random.random()</li>
			<li>Suppose the sector index is i, set the angular value for jth sensor node as ang_{i,j}=(60*random.random() + A_i - 60) * 2 * pi / 360</li>
			<li>Set the Cartesian coordinates of the jth sensor node in ith sector as 
			x_{i,j}=l_{i,j}*cos(ang_{i,j}) 
			y_{i,j}=l_{i,j}*sin(ang_{i,j})</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
Supplementary Figure 2: Script2. Script to configure the two dimension position locations for each node by sector. <a href="https://www.jove.com/files/ftp_upload/64930/Supplementary Figure 2.pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 3: Script3. Script to configure each node position values within 1 sector. <a href="https://www.jove.com/files/ftp_upload/64930/Supplementary Figure 3.pdf" target="_blank">Please click here to download this File.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/64930/64930fig03.jpg" /> 
Figure 3: Node positions generated and stored separated into 6 files each corresponding to one sector. Two-dimension position locations are saved in 6 posdata+&#39;idx&#39; files. Each presents a sector. <a href="https://www.jove.com/files/ftp_upload/64930/64930fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/64930/64930fig04.jpg" /> 
Figure 4: Node positions stored in sector 0. The positions are in two dimensions and generated using a uniform random generator. The first column is the horizontal locations, and the second column is the vertical locations. <a href="https://www.jove.com/files/ftp_upload/64930/64930fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
5. Preparing the initial energy levels
<ol>
	<li>Prepare the initial energy levels for all 198 sensor nodes. Assign half of them with initial energy at 0.5 J and the other half with initial energy at 1 J. Create an array to store each node&#39;s energy level, and use a loop to assign cells sequenced in even numbers the value 1 and those sequenced in odd numbers the value of 0.5. Supplementary Figure 4 shows the Python codes, and the result is shown in Figure 5.</li>
</ol>
Supplementary Figure 4: Script4. Script to assign half of the node&#39;s energy of 1 joule and the other 0.5 joules. <a href="https://www.jove.com/files/ftp_upload/64930/Supplementary Figure 4.png" target="_blank">Please click here to download this File.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/64930/64930fig05.jpg" /> 
Figure 5: Energy_buffer initial assignment. Half of the nodes are assigned with energy 1 joule, while the other halves are assigned with 0.5 joules. <a href="https://www.jove.com/files/ftp_upload/64930/64930fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
6. Preparing Advanced_Leach algorithm script (Figure 6 and Figure 7)
<ol>
	<li>Prepare a functional script where the cluster head is selected and the cluster is formed. 
	NOTE: The cluster is selected using a loop on the condition that the number of cluster heads selected is less than the overall number of nodes divided by 6. The condition is to ensure that within each cluster, the source node amount is equal to or less than 6. Within the loop, each node is assigned a random number between [0,1]. Those smaller than a given criteria number become the cluster head, while others become the source nodes. The detailed procedure is shown in Supplementary Figure 5. Then given a fixed pool of cluster heads, the rest of the source nodes select their cluster heads within the shortest distance, given that the cluster head has not hosted more than 6 source nodes yet. The detailed procedure is shown in Supplementary Figure 6.
	<ol>
		<li>Set T_n=P/(1-P*(count%(1/P))), where P = 0.2 (the proportional rate of the number of cluster heads to the overall network size) and the count is the amount of transmission round up to date.</li>
		<li>For each sensor node, attain a random number between[0,1] threshold_rm = random.random()
		<ol>
			<li>If threshold_rm is less than T_n, select this sensor node as the cluster head.</li>
		</ol>
		</li>
		<li>For each of the non-cluster_head nodes, select the closest cluster head sensor node to it as its cluster head. Given a fixed pool of cluster heads, the rest of the source nodes select their cluster heads within the shortest distance, given that the cluster head has not hosted more than 6 source nodes yet. The detailed procedure is shown in Supplementary Figure 6.</li>
	</ol>
	</li>
	<li>Prepare the command lines to calculate the energy depletion process across the whole network for this round. For each run of the algorithm that completes one batch of deliveries of the packets from source nodes to the sink, the energy storage array as prepared will be updated to have reduced cell by cell in values. The energy consumption along the path will be the summation of energy consumption per node-to-node route, which is calculated according to an energy model1. The detailed procedure is shown in Supplementary Figure 7.</li>
	<li>Calculate the required transmission round metrics. 
	NOTE: Per each run of the algorithm to complete one batch of packet delivery, the energy array is updated, the run amount and the number of drained-out nodes are counted. If the value is larger or equal to 1, then FND (first node die) is equal to current run amount. If the value is larger or equal to half of the node amount, then HND(half node die) is equal to current run amount. If the value is equal to the overall node amount, then AND(all node die) is equal to current run amount. The detailed procedure is shown in Supplementary Figure 8.</li>
</ol>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/64930/64930fig06.jpg" /> 
Figure 6: Cluster head array. The sequence numbers of the nodes that have been selected to be the cluster heads. <a href="https://www.jove.com/files/ftp_upload/64930/64930fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/64930/64930fig07.jpg" /> 
Figure 7: Cluster head index array. As there are six sectors, each with 33 sensor nodes, in the cluster head index array, the number indicates the sequence number of the cluster head the corresponding sensor node belongs to. The position index of the array corresponds to the sequence number of each sensor node. For the sensor node that is selected as the cluster head, the number assigned to its slot in the array is the sequence number of itself. <a href="https://www.jove.com/files/ftp_upload/64930/64930fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Figure 5: Script5. Script to select the cluster head. <a href="https://www.jove.com/files/ftp_upload/64930/Supplementary Figure 5.png" target="_blank">Please click here to download this File.</a>
Supplementary Figure 6: Script6. Script to assign source nodes to clusters. <a href="https://www.jove.com/files/ftp_upload/64930/Supplementary Figure 6.png" target="_blank">Please click here to download this File.</a>
Supplementary Figure 7: Script7. Script to update the energy buffer for all source nodes via reduction of the amount of energy consumed through transmission. <a href="https://www.jove.com/files/ftp_upload/64930/Supplementary Figure 7.png" target="_blank">Please click here to download this File.</a>
Supplementary Figure 8: Script8. Script to calculate the amount of rounds up to which the first node dies, and half of the nodes die. <a href="https://www.jove.com/files/ftp_upload/64930/Supplementary Figure 8.png" target="_blank">Please click here to download this File.</a>
7. Preparing hybrid quantum algorithm script
<ol>
	<li>Prepare a functioning script where the cluster head is selected and the cluster head is formed.
	<ol>
		<li>As the maximum cluster size is 6 in this experiment1, ensure that the amount of cluster heads is no less than current_valid_node_amount/6, the selection procedure will be run in a loop until this criterion is met. 
		NOTE: If current_valid_node_amount is no larger than 6, then these valid nodes form one and only cluster themselves.</li>
		<li>For each of the non-cluster_head_valid nodes, calculate the distance of it to each of the selected cluster heads, and assign to it the cluster head whose cluster size has not exceeded 6 where the distance value is the smallest. 
		NOTE: In Figure 8, all the non-cluster_head_valid nodes&#39; distances to the selected cluster head 24 are calculated, and all the selected cluster head is displayed in Figure 9. Figure 10 displays all the nodes are assigned to their corresponding cluster head, and Figure 11 shows grouping each cluster&#39;s member nodes in a vector array.</li>
	</ol>
	</li>
	<li>Prepare the sub-function script, where the routing optimization problem per cluster is formed and submitted to the D-wave API (Figure 12). The routing paths are computed cluster by cluster.</li>
	<li>Using Python script, calculate the energy depletion process across the whole network to quantitatively evaluate the algorithm by network lifetime in terms of the number of transmission rounds. 
	NOTE: For each run of the algorithm that completes one batch of deliveries of the packets from source nodes to the sink, the energy storage array as prepared will be updated to have reduced cell by cell in values. The energy consumption along the path will be the summation of energy consumption per node-to-node route, calculated according to an energy model1. The detailed procedure is shown in Supplementary Figure 7.</li>
	<li>Using Python script, record the moment when the first node is drained out and when half the nodes are drained out. The detailed procedure is shown in Supplementary Figure 8.</li>
</ol>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/64930/64930fig08.jpg" /> 
Figure 8: toClusterHeadDistance Array for non-cluster_head node with index 24. The first column is the distance and the second column is the cluster head index number <a href="https://www.jove.com/files/ftp_upload/64930/64930fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/64930/64930fig09.jpg" /> 
Figure 9: CHID_buff array. Sequence numbers of the sensor nodes that are selected as the cluster heads. <a href="https://www.jove.com/files/ftp_upload/64930/64930fig09large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/64930/64930fig10.jpg" /> 
Figure 10 CHIdx_buff array. Assigned cluster head sensor nodes&#39; sequence number to each corresponding sensor node. <a href="https://www.jove.com/files/ftp_upload/64930/64930fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 11" class="xfigimg" src="/files/ftp_upload/64930/64930fig11.jpg" /> 
Figure 11: CH_BUFF array. Cluster group per cluster head sensor nodes corresponding to the array CHID_buff. Each cluster group consists of 0 or more than 0 sensor nodes. Each cluster group array displays the sequence numbers of sensor nodes that are in it. <a href="https://www.jove.com/files/ftp_upload/64930/64930fig11large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 12" class="xfigimg" src="/files/ftp_upload/64930/64930fig12.jpg" /> 
Figure 12: Routing path computation per sector. For each sector, the routing paths for all source nodes are computed. <a href="https://www.jove.com/files/ftp_upload/64930/64930fig12large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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The current state-of-the-art commercial quantum processor can be used in computational problems of any network topology1. Quantum processor application is not constrained by the number of physical qbits any of the quantum processors has been able to implement. 
In sensor network lifetime prolongation design, the results show an advancement in method to achieve even longer network life by using a quantum processor. The results imply that quantum advantage is ready to be ...

Energy conservation in sensor networks has been a very critical issue in design1. Classical methods normally tackle the problem using an ad hoc approach2,3,4,5,6. That said, these methods emulate the sensor nodes as individually managed intelligent assets that could also cooperate to serve both the interests of the individual and the community. Due to the volatile environment where sensors work, in some works, random algorithms are introduced in order to capture the environmental uncertainties, while in others, bio-intelligence is borrowed to devise heuristic algorithms that could achieve common sense acceptable results7. To illustrate further, for those random algorithms, for one hand, environmental uncertainties might not be as random as the random sequence generated by a classical CPU, for the other hand, even if the environmental uncertainties are absolutely random, they could not be captured by the random process simulator generated by classical CPU; for those bio-intelligence algorithms, first of all, no rigorous mathematical analysis has been derived to make a conceptual proof work, secondly, the convergence to truth or the error tolerance boundary can only be configured given an informed ground truth - though a significant amount of works in literature have demonstrated to some extent these heuristic algorithms work, for one thing, these algorithms are analyzed (not simulated) against well-defined use case scenarios, they stops at certain criteria that are still worth pondering in further research, for another, as said before, a majority of the algorithms have not been validated against software simulation that can be more readily deployed in the microprocessors that make a sensor into its being8.
We do not consider machine learning (ML) here because it needs to employ data analytics which requires a relatively large volume of computational power that is not portable in sensor devices9.
To address the above-mentioned concerns, we provide a hybrid quantum algorithm. The algorithm is hybrid in that the cluster head selection mechanism is implemented using a classical random algorithm during the routing computations conducted using a quantum processor once the network topology is set up. The method is justified as follows: (1) As discussed in the first paragraph regarding the environmental uncertainties, we do not want to endeavor further to apply a quantum sequence generator to capture the environmental dynamic because it might be historically traceable. The environmental dynamic that can be historically traceable has been justified by various machine learning research works in network science. For the current stage, we stay with the classical approach. (2) The exact method that relies on abstract mathematical analysis guarantees to arrive at ground truth. Quantum experimental physics has so far been sophisticatedly supported by physical mathematics. Moreover, algorithm applications like the Shor algorithm10 have existed to prove this rounded theory.
An adequate amount of literature survey is provided below for comparison. The HEESR protocol proposed11 has demonstrable merits in results, but the authors have specified the simulation configuration parameters well, for example, the exact random distribution function of the node position, the proper justification of the cluster head percentage p (0.2%), and the scaling parameter for distribution of energy level (1-2 joules) among nodes a_i. It prohibited the author from proceeding further to duplicate the experiments and conduct the comparison. The Power routing mechanism12 employs the curve fitting method to approximate converged continuous functions from discrete data sets obtained from unspecified sample space for determinants that impact the decision process of the optimal network routing. The curve fitting method13 requires prior information on the network topology. Real circumstances might not have prior information readily available. Even when prior information exists, network topology might not be regular enough to be able to be mapped onto fitting curves that are able to facilitate derivable computation. Following the same logic, DORAF protocol14 has not justified how and why to borrow the Boltzmann function and Logistic Function to approximate the network determinants. Ismail et al.15 have provided a sound reference for future research endeavors into energy-efficient routing protocol design in the underwater network.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Dell Laptop</td>
			<td>Dell</td>
			<td>N/A</td>
			<td></td>
		</tr>
		<tr>
			<td>Ubuntu 18.04.6 LTS</td>
			<td>Canonical Ltd</td>
			<td>18.04.6 LTS</td>
			<td></td>
		</tr>
		<tr>
			<td>Python3.8</td>
			<td>Python Software Foundation</td>
			<td>3.8.0</td>
			<td></td>
		</tr>
		<tr>
			<td>Dwave QPU</td>
			<td>Dwave</td>
			<td>https://docs.ocean.dwavesys.com/en/stable/overview/install.html</td>
			<td></td>
		</tr>
	</tbody>
</table>

large scale energy efficient sensor network routing using a quantum processor unit

The sensor network energy conservation method, which is a usage hybrid of a classical computer and quantum processor, has proved to perform better than the heuristic algorithm using a classical computer. In this manuscript, the technical context for the significance of the method is presented and justified. Then the experimental steps are demonstrated in an operational sequence with illustrations if ever needed. The method has been validated by positive results across a randomly generated sample set of network topologies. The successful experimental results of this method have provided a better approach for sensor network lifetime maximization problems and demonstrated that the current state of art quantum processor has been able to solve large practical engineering problems with merits that override current methods in the literature. In other words, quantum advantage can be exploited to best efforts. It has gone beyond the stage of proof of concept to proof of feasibility.

Results from one run sample are shown in Table 2, Table 3, and Table 4. The detailed datasets for the three batches of data are available in the Supplementary Data 1 folder.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="3">Dataset 1</td>
		</tr>
		<tr>
			<td>198 nodes in a circular area with a radius of 50m</td>
			<td>Hybrid Quantum Algorithm</td>
			<td>A...

Watch this Scientific Journal Video about Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit at JoVE.com

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

This study provides a method to use a quantum processor unit to compute the routes for various traffic dynamics that work to outperform classical methods in literature to maximize network lifetime.

The sensor network energy conservation method, which is a usage hybrid of a classical computer and quantum processor, has proved to perform better than ...

large-scale-energy-efficient-sensor-network-routing-using-quantum

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440 Views.  Imperial College London. This study provides a method to use a quantum processor unit to compute the routes for various traffic dynamics that work to outperform classical methods in literature to maximize network lifetime.

Video: Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Terahertz Microfluidic Sensing Using a Parallel-plate Waveguide Sensor

Refractive index (RI) sensing is a powerful noninvasive and label-free sensing technique for the identification, detection and monitoring of microfluidic samples with a wide range of possible sensor designs such as interferometers and resonators 1,2. Most of the existing RI sensing applications focus on biological materials in aqueous solutions in visible and IR frequencies, such as DNA hybridization and genome sequencing. At terahertz frequencies, applications include quality control, monitoring of industrial processes and sensing and detection applications involving nonpolar materials.
Several potential designs for refractive index sensors in the terahertz regime exist, including photonic crystal waveguides 3, asymmetric split-ring resonators 4, and photonic band gap structures integrated into parallel-plate waveguides 5. Many of these designs are based on optical resonators such as rings or cavities. The resonant frequencies of these structures are dependent on the refractive index of the material in or around the resonator. By monitoring the shifts in resonant frequency the refractive index of a sample can be accurately measured and this in turn can be used to identify a material, monitor contamination or dilution, etc.
The sensor design we use here is based on a simple parallel-plate waveguide 6,7. A rectangular groove machined into one face acts as a resonant cavity (Figures 1 and 2). When terahertz radiation is coupled into the waveguide and propagates in the lowest-order transverse-electric (TE1) mode, the result is a single strong resonant feature with a tunable resonant frequency that is dependent on the geometry of the groove 6,8. This groove can be filled with nonpolar liquid microfluidic samples which cause a shift in the observed resonant frequency that depends on the amount of liquid in the groove and its refractive index 9.
Our technique has an advantage over other terahertz techniques in its simplicity, both in fabrication and implementation, since the procedure can be accomplished with standard laboratory equipment without the need for a clean room or any special fabrication or experimental techniques. It can also be easily expanded to multichannel operation by the incorporation of multiple grooves 10. In this video we will describe our complete experimental procedure, from the design of the sensor to the data analysis and determination of the sample refractive index.

The procedure for implementing a refractive index sensor for terahertz frequencies based on a grooved parallel-plate waveguide geometry is described here. The method yields a measurement of the refractive index of a small volume of liquid through monitoring of the shift in the resonant frequency of the waveguide structure

Refractive index (RI) sensing is a powerful noninvasive and label-free sensing technique for the identification, detection and monitoring of microfluidic ...

Gradient Echo Quantum Memory in Warm Atomic Vapor

Gradient echo memory (GEM) is a protocol for storing optical quantum states of light in atomic ensembles. The primary motivation for such a technology is that quantum key distribution (QKD), which uses Heisenberg uncertainty to guarantee security of cryptographic keys, is limited in transmission distance. The development of a quantum repeater is a possible path to extend QKD range, but a repeater will need a quantum memory. In our experiments we use a gas of rubidium 87 vapor that is contained in a warm gas cell. This makes the scheme particularly simple. It is also a highly versatile scheme that enables in-memory refinement of the stored state, such as frequency shifting and bandwidth manipulation. The basis of the GEM protocol is to absorb the light into an ensemble of atoms that has been prepared in a magnetic field gradient. The reversal of this gradient leads to rephasing of the atomic polarization and thus recall of the stored optical state. We will outline how we prepare the atoms and this gradient and also describe some of the pitfalls that need to be avoided, in particular four-wave mixing, which can give rise to optical gain.

The gradient echo memory is a protocol for storing optical quantum states of light in atomic ensembles. Quantum memory is a key element of a quantum repeater, which can extend the range of quantum key distribution. We outline the operation of the scheme when implemented in a 3-level atomic ensemble.

Gradient  echo memory (GEM) is a protocol for storing optical quantum states of light in  atomic ensembles. The primary motivation for such a technology ...

Analysis of Contact Interfaces for Single GaN Nanowire Devices

Single GaN nanowire (NW) devices fabricated on SiO2 can exhibit a strong degradation after annealing due to the occurrence of void formation at the contact/SiO2 interface. This void formation can cause cracking and delamination of the metal film, which can increase the resistance or lead to a complete failure of the NW device. In order to address issues associated with void formation, a technique was developed that removes Ni/Au contact metal films from the substrates to allow for the examination and characterization of the contact/substrate and contact/NW interfaces of single GaN NW devices. This procedure determines the degree of adhesion of the contact films to the substrate and NWs and allows for the characterization of the morphology and composition of the contact interface with the substrate and nanowires. This technique is also useful for assessing the amount of residual contamination that remains from the NW suspension and from photolithographic processes on the NW-SiO2 surface prior to metal deposition. The detailed steps of this procedure are presented for the removal of annealed Ni/Au contacts to Mg-doped GaN NWs on a SiO2 substrate.

A technique was developed that removes Ni/Au contact metal films from their substrate to allow for the examination and characterization of the contact/substrate and contact/NW interfaces of single GaN nanowire devices.

Single GaN nanowire (NW) devices fabricated on SiO2 can exhibit  a strong degradation after annealing due to the  occurrence of void formation at the ...

Using Synchrotron Radiation Microtomography to Investigate Multi-scale Three-dimensional Microelectronic Packages

Synchrotron radiation micro-tomography (SR&#181;T) is a non-destructive three-dimensional (3D) imaging technique that offers high flux for fast data acquisition times with high spatial resolution. In the electronics industry there is serious interest in performing failure analysis on 3D microelectronic packages, many which contain multiple levels of high-density interconnections. Often in tomography there is a trade-off between image resolution and the volume of a sample that can be imaged. This inverse relationship limits the usefulness of conventional computed tomography (CT) systems since a microelectronic package is often large in cross sectional area 100-3,600 mm2, but has important features on the micron scale. The micro-tomography beamline at the Advanced Light Source (ALS), in Berkeley, CA USA, has a setup which is adaptable and can be tailored to a sample&#39;s properties, i.e., density, thickness, etc., with a maximum allowable cross-section of 36 x 36 mm. This setup also has the option of being either monochromatic in the energy range ~7-43 keV or operating with maximum flux in white light mode using a polychromatic beam. Presented here are details of the experimental steps taken to image an entire 16 x 16 mm system within a package, in order to obtain 3D images of the system with a spatial resolution of 8.7 &#181;m all within a scan time of less than 3 min. Also shown are results from packages scanned in different orientations and a sectioned package for higher resolution imaging. In contrast a conventional CT system would take hours to record data with potentially poorer resolution. Indeed, the ratio of field-of-view to throughput time is much higher when using the synchrotron radiation tomography setup. The description below of the experimental setup can be implemented and adapted for use with many other multi-materials.

For this study synchrotron radiation micro-tomography, a non-destructive three-dimensional imaging technique, is employed to investigate an entire microelectronic package with a cross-sectional area of 16 x 16 mm. Due to the synchrotron&#39;s high flux and brightness the sample was imaged in just 3 min&#160;with an 8.7 &#181;m spatial resolution.

Synchrotron radiation micro-tomography (SR&#181;T) is a non-destructive three-dimensional (3D) imaging technique that offers high flux for fast data ...

How to Build a Vacuum Spring-transport Package for Spinning Rotor Gauges

The spinning rotor gauge (SRG) is a high-vacuum gauge often used as a secondary or transfer standard for vacuum pressures in the range of 1.0 x 10-4 Pa to 1.0 Pa. In this application, the SRGs are frequently transported to laboratories for calibration. Events can occur during transportation that change the rotor surface conditions, thus changing the calibration factor. To assure calibration stability, a spring-transport mechanism is often used to immobilize the rotor and keep it under vacuum during transport. It is also important to transport the spring-transport mechanism using packaging designed to minimize the risk of damage during shipping. In this manuscript, a detailed description is given on how to build a robust spring-transport mechanism and shipping container. Together these form a spring-transport package. The spring-transport package design was tested using drop-tests and the performance was found to be excellent. The present spring-transport mechanism design keeps the rotor immobilized when experiencing shocks of several hundred g (g = 9.8 m/sec2 and is the acceleration due to gravity), while the shipping container assures that the mechanism will not experience shocks greater than about 100 g during common shipping mishaps (as defined by industry standards).

Here we describe how to build a robust spring-transport mechanism for a spinning rotor gauge. This device securely immobilizes the rotor and keeps it under vacuum during transportation. We also describe packaging that minimizes the risk of damage during transport. Tests show our design works for typical shocks during transport.

The spinning rotor gauge (SRG) is a high-vacuum gauge often used as a secondary or transfer standard for vacuum pressures in the range of 1.0 x 10-4 Pa to ...

Resonance Fluorescence of an InGaAs Quantum Dot in a Planar Cavity Using Orthogonal Excitation and Detection

The ability to perform simultaneous resonant excitation and fluorescence detection is important for quantum optical measurements of quantum dots (QDs). Resonant excitation without fluorescence detection &#8211; for example, a differential transmission measurement &#8211; can determine some properties of the emitting system, but does not allow applications or measurements based on the emitted photons. For example, the measurement of photon correlations, observation of the Mollow triplet, and realization of single photon sources all require collection of the fluorescence. Incoherent excitation with fluorescence detection &#8211; for example, above band-gap excitation &#8211; can be used to create single photon sources, but the disturbance of the environment due to the excitation reduces the indistinguishability of the photons. Single photon sources based on QDs will have to be resonantly excited to have high photon indistinguishability, and simultaneous collection of the photons will be necessary to make use of them. We demonstrate a method to resonantly excite a single QD embedded in a planar cavity by coupling the excitation beam into this cavity from the cleaved face of the sample while collecting the fluorescence along the sample&#39;s surface normal direction. By carefully matching the excitation beam to the waveguide mode of the cavity, the excitation light can couple into the cavity and interact with the QD. The scattered photons can couple to the Fabry-Perot mode of the cavity and escape in the surface normal direction. This method allows complete freedom in the detection polarization, but the excitation polarization is restricted by the propagation direction of the excitation beam. The fluorescence from the wetting layer provides a guide to align the collection path with respect to the excitation beam. The orthogonality of the excitation and detection modes enables resonant excitation of a single QD with negligible laser scattering background.

Resonant excitation of a single self-assembled quantum dot can be achieved using an excitation mode orthogonal to the fluorescence collection mode. We demonstrate a method using the waveguide and Fabry-Perot modes of a planar microcavity surrounding the quantum dots. The method allows complete freedom in the detection polarization.

The ability to perform simultaneous resonant excitation and fluorescence detection is important for quantum optical measurements of quantum dots (QDs). ...

Enhanced Electron Injection and Exciton Confinement for Pure Blue Quantum-Dot Light-Emitting Diodes by Introducing Partially Oxidized Aluminum Cathode

Stable and efficient red (R), green (G), and blue (B) light sources based on solution-processed quantum dots (QDs) play important roles in next-generation displays and solid-state lighting technologies. The brightness and efficiency of blue QDs-based light-emitting diodes (LEDs) remain inferior to their red and green counterparts, due to the inherently unfavorable energy levels of different colors of light. To solve these problems, a device structure should be designed to balance the injection holes and electrons into the emissive QD layer. Herein, through a simple autoxidation strategy, pure blue QD-LEDs which are highly bright and efficient are demonstrated, with a structure of ITO/PEDOT:PSS/Poly-TPD/QDs/Al:Al2O3. The autoxidized Al:Al2O3 cathode can effectively balance the injected charges and enhance radiative recombination without introducing an additional electron transport layer (ETL). As a result, high color-saturated blue QD-LEDs are achieved with a maximum luminance over 13,000 cd m-2, and a maximum current efficiency of 1.15 cd A-1. The easily controlled autoxidation procedure paves the way for achieving high-performance blue QD-LEDs.

A protocol is presented for fabricating high-performance, pure blue ZnCdS/ZnS-based quantum dots light-emitting diodes by employing an autoxidized aluminum cathode.

Stable and efficient red (R), green (G), and blue (B) light sources based on solution-processed quantum dots (QDs) play important roles in next-generation ...

Generation and Coherent Control of Pulsed Quantum Frequency Combs

We present a method for the generation and coherent manipulation of pulsed quantum frequency combs. Until now, methods of preparing high-dimensional states on-chip in a practical way have remained elusive due to the increasing complexity of the quantum circuitry needed to prepare and process such states. Here, we outline how high-dimensional, frequency-bin entangled, two-photon states can be generated at a stable, high generation rate by using a nested-cavity, actively mode-locked excitation of a nonlinear micro-cavity. This technique is used to produce pulsed quantum frequency combs. Moreover, we present how the quantum states can be coherently manipulated using standard telecommunications components such as programmable filters and electro-optic modulators. In particular, we show in detail how to accomplish state characterization measurements such as density matrix reconstruction, coincidence detection, and single photon spectrum determination. The presented methods form an accessible, reconfigurable, and scalable foundation for complex high-dimensional state preparation and manipulation protocols in the frequency domain.

A protocol is presented for the practical generation and coherent manipulation of high-dimensional frequency-bin entangled photon states using integrated micro-cavities and standard telecommunications components, respectively.

We present a method for the generation and coherent manipulation of pulsed quantum frequency combs. Until now, methods of preparing high-dimensional ...

An Efficient Method for Selective Desalination of Radioactive Iodine Anions by Using Gold Nanoparticles-Embedded Membrane Filter

Here, we demonstrate a detail protocol for the preparation of nanomaterials-embedded composite membranes and its application to the efficient and ion-selective removal of radioactive iodines. By using citrate-stabilized gold nanoparticles (mean diameter: 13 nm) and cellulose acetate membranes, gold nanoparticle-embedded cellulose acetate membranes (Au-CAM) have easily been fabricated. The nano-adsorbents on Au-CAM were highly stable in the presence of high concentration of inorganic salts and organic molecules. The iodide ions in aqueous solutions could rapidly be captured by this engineered membrane. Through a filtration process using an Au-CAM containing filter unit, excellent removal efficiency (&gt;99%) as well as ion-selective desalination result was achieved in a short time. Moreover, Au-CAM provided good reusability without significant decrease of its performances. These results suggested that the present technology using the engineered hybrid membrane will be a promising process for the large-scale decontamination of radioactive iodine from liquid wastes.

An efficient method for the rapid and ion-selective desalination of radioactive iodine in several aqueous solutions is described by using gold nanoparticles-immobilized cellulose acetate membrane filters.

Here, we demonstrate a detail protocol for the preparation of nanomaterials-embedded composite membranes and its application to the efficient and ...

Measuring Sub-23 Nanometer Real Driving Particle Number Emissions Using the Portable DownToTen Sampling System

The current particle size threshold of the European Particle Number (PN) emission standards is 23 nm. This threshold could change because future combustion engine vehicle technology may emit large amounts of sub-23 nm particles. The Horizon 2020 funded project DownToTen (DTT) developed a sampling and measurement method to characterize particle emissions in this currently unregulated size range. A PN measurement system was developed based on an extensive review of the literature and laboratory experiments testing a variety of PN measurement and sampling approaches. The measurement system developed is characterized by high particle penetration and versatility, which enables the assessment of primary particles, delayed primary particles, and secondary aerosols, starting from a few nanometers in diameter. This paper provides instruction on how to install and operate this Portable Emission Measurement System (PEMS) for Real Drive Emissions (RDE) measurements and assess particle number emissions below the current legislative limit of 23 nm.

Presented here is the DownToTen (DTT) portable emission measurement system to assess real driving automotive emissions of sub-23 nm particles.