Panel Discussion of Ecosystem for Transitioning from Lab to Production

Panel Discussion of Ecosystem for Transitioning from Lab to Production

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Okay. So while everybody's getting getting set up sorry Joe um so, this is a panel discussion this is an opportunity, both for our speakers, and members. In the audience both, here and if you're viewing on the webcast you're, able to submit questions to, try. To draw out in more detail some, of the issues that brought up by the speakers at the speakers this afternoon will address which are a lot more about testing and, some physical sensor issues. That. Either things are in more detail or maybe points in which you're sure there's not a single standard answer that's, what we're hoping that this panel discussion will, let us sort of begin, to set the stage of the major factors that we're going to consider during the scrimmage tomorrow and also, include in the report that we're hoping will provide some overview or some idea of really, what are the next steps or what's the the major considerations, as we start thinking about manufacturing nano, sensors so that being said I'm gonna hand it to Joe who's going to moderate the panel and I think you have some questions to begin. Thanks. To our group here we have some really good questions to, start, out and, one. Of the things, question. One addresses, a supply, chain and supplies. Of nanomaterials, and believe. Me this is basic, to using any nanomaterials. In a product, of. Course the process can be chosen and the process, needs to be repeatable, and we, work on quality and we work on throughput, at we work on yield. For. Different processes but, if, you don't have a good, source of supply. For, the nanomaterials. And my. Story is I started, using helix. Single. Walled carbon nanotubes. To make, the ozone. Sensors, that we finally took to market and, last. Year they stopped answering their phone. And. I thought what, but. So so, we had to qualify another, material and that's, not easy, we, bought nanotubes, from three, or four different suppliers, we. Finally wound up with us nano we headed by three batches from them and all. Three batches came in different they were different processing. Different. Hydrophobicity. So we had to change the ink formula, they, had different bulk densities, so the, loading was different for the same amount. Of material and so. These kind of difficulties, play. Not, only startups, but even a large company who would want to use these materials and so the first question has to do for the group if you, could address the, supply chain how, to choose, and source nanomaterials. Especially. With respect to integrating, them into. Your. Or any, fabrication. Process, so. Who would like to start. Very. Similar stories. Not. Just not a particle nanotubes, but metals, also we. We had a company I'm not gonna mention company, names but. Lately. We we, have. Used. Zinc, selenide particles, and and the. First batch was really nice you know nano. Nano scale 10. Nanometers or less next. Batch was 200 nanometers, or more and we're, trying to use these particles, to build 50. Nanometer structures. Because. That's, right so. So, it it took him a long time took him six months actually to get back to to the supply that we wanted with, nanotubes I mean we had some companies given. Us semiconducting, then, semiconducting. Nanotubes what we thought was semiconducting, tubes they were multi. Walled and why. Not semiconducting, and we. Fought with them and they would they would say no and so.

We Had, to do, Raman we, had to do TM. And, and. SEM. And, we have to put the device to show them that it was not semiconducting. So. So, you, run into this all the time and and the best protection is to have very clear spec when you buy something and you, can tell them right up front if it, does not meet these specs which is actually also your advertised, specs right that's, yeah. We're. Gonna return it you know and go, for somewhere else and it's better to have, alternate. You, know not just depend on one source because like, you like, you said you know overnight. They can disappear and then, you have to start over and it's very expensive to qualify something different. Okay. Yeah. My name is Abhishek Mehta I'm founder and CEO of n5, sensors. Just. A brief one-line intro, to our company we, make micro. Scale gas sensors, on a chip, like single chip sensors, I. Guess. I. Yes. So the answer I mean to this question supply chain management it's, actually, so, important, that it. Can, almost make. Or. Break your company, especially as a start-up if the first time you're trying to do this and you're, you, have to keep this in mind that at the end of the day if. You're lucky enough to establish your own manufacturing. Facility that's, that's great, but for most of us it's it's incredibly. Challenging so that means you'll be looking for a contract, manufacturer. Maybe. Somewhere, in the US somewhere, outside who knows but then, doing. That you also have to ensure that the. Manufacturer. That you are trying to work, with they. Will be ok handling. The materials, that you, are introducing into, your process, so for. Example. You. Know the product, that we manufacture. Inside. We put like about 35. 40 different processes, on top of each other to deliver, the final product, but, starting, material, is a wafer, that. We get from an epitaxial, vendor, in Europe, and then. You in in between you have chemicals. You have, targets. From ordered. From different. Companies different vendors. You. Can list like there is a huge excel, sheet with hundreds, and hundreds of line items go in and you're, trying to control every, single aspect, of that processing. Process, line we call it from. From wafer, specs, including.

Like You know. RMS. Thickness, and whatnot. So. And then at the end of the day you have to understand, that you have to take all of that because, if you want to scale your company and make some money out of it that that's the whole point for the, first people that are, trying to you know start, a company and build, it so, you have to transfer, all of that into, commercial. Manufacturing, facility, and most. Of the times they. Don't want to deviate from, what they have been doing for the last ten years so. That has been a significant. Challenge for us they said no we can't do or, we can't put this material in one of our tools because this is our main business. Line, and that. You. Better know this you. Know now then, a, year. Later when, you have invest about million dollars transitioning. The manufacturing, the contract manufacturer, and they, said no we can't do that so my. Advice, and you know learn, hard hard, ways that you have to talk to all these contract, manufacturers, and the vendors and figure. Out what, is compatible, down the road because it's not now it's not today but you're looking at future, once. You have a product like in your scaling up so. It starts early and just basically, qualify, everything, that can go into production and, will go into production and, compatible. With the tool chains and, and. And you ensure the vendors, are going to be around and you've, wanted them to scale up so, it's. Sammy. Donna I am a principal research scientist, at you, know a Technology Research Center in his Harvard candy kit we. Are doing a lot of work in air space and building industries application. And I will describe at an afternoon session. Ok. So I'm a I'm the assistant, professor from Kansas, State University, actually and startup company. As well with my post down in, the we. Tried, to focus. On the, nanoparticles. And we hope to have this, more. Power most effectively particles, that can integrate it with the sensor part so, we, tried. To have the add the value to the particle to form the integrated, system so, that's really. Starting, that company had to first establish. My. Name is Ahmad boost inna I'm the director, of the NSF, nanoscale. Science engineer, Center at, Northeastern. University. I'm. Susan, Rose Pearson, I'm the director of the Navy Technology, Center for safety and survivability, and my. Expertise. Is in sensor, arrays and. Multivariate. Data analysis. And Censored, testing. I'm. Joe, Stoddard and I'm, currently, with speck. Sensors, and kwj. Engineering in. California. And that's. A perfect lead-in to our next, question, which. Is, about making. Multiple, measurements. Which. You can make through multiple sensors, or using a single sensor in multiple, conditions. Of measurement, to. Improve the quality of data both, in terms of multi, variate, data, analysis. Or I've probably. Formatted. To. Get more data that's. Suitable, for multi, valid variant.

Analysis, Which, can pull out information, from your data and how. To, overcome variability. In measurements, and in, sensors. Stability. Over time and temperature and other things and the, perfect person, next, to me I'll go back this way as, soon as it can address. Multivariate. Sand sensors. The. Sensor, array data. Is very difficult, as you know and it. Dates back to many. Years ago how do, we handle this how do you design a sensor, array and we've. Been struggling with this for, decades. Recent. Papers. Have. Been on the limit of recognition, as opposed. To the limit of detection or. The limit of quantitation. Because. When. You're dealing with multivariate. First. You have to have the pattern, and the pattern may, change, with. Concentration. The. Other. Issue, is. I've. Always, believed, that it's a good idea to put multimodal. Sensors. Together so. That the information, that, you're getting. Is. Very. That you're measuring different. Parameters. About that, chemical, and not, just. Similar. Parameters. As you would have if you did an array of just one type of sensor, and so. But that introduces. Its own challenges. How do you, date, a fuse. Information. That, is coming, from multiple sources because, many times they, have different, response, times they have, varying. Parameters, and Joe, and I had this experience, many years ago when we were looking at some electrochemical. Sensors and some. Sensors, would dominate the. Entire. Pattern so, then we had to do pattern normalization. Before. We could even identify. The, chemical. To. Move forward, the. Some. Research, that's going on in my group, at the Naval Research Laboratory, is, in design theory. And we're, trying to take information, theory. And apply, it to sensors, and some. Of the first information. That's coming out of that is more. Is not always better. You. Can put a lot of sensors, together and in some cases all you do is increase, the level. Of false, alarms, and so. You're. Not actually getting. Additional. Recognition. So. You have to do this in a very intelligent, way and many, times your sensor, array is no. Better than, the weakest sensor, in the, array depending. On how, you do, your algorithm. Development so. As you, increase, the number of sensors. This. Can be a very complex. Approach. To sensors, and, the. Whole, field of statistics. And and so, forth has, to be expanded, from the univariate, measurements. To. The multivariate, measurements. I. Want. To say one thing that Susan. Taught me in the mid-1980s. When. I was doing this work I was a scientist. And she, was working with Peter, at Penn, State doing. At. That time very this. Is very new stuff to do multivariate. And. Pattern. Recognition and, principle component, analysis, and these kinds of things for. Datasets and. The. One thing that struck. Me as important, which I never forgot was in. Order to do pattern recognition, you can get that from any group of sensors, or sensory signals. Operating. Them differently or whatever but, if you do that for a series of chemicals, in.

A Certain matrix. That's. Your. Data set if. You then take an unknown, and you, do it in the atmosphere, in a different, matrix. That. Pattern, is not a member, of that data set mathematically. And it's, incorrect, in, terms. Of rigorous mathematics. To, assume to interpret that pattern, with, that principal. Component, analysis, so. You either have to do, principal. Component, analysis, or multivariate for, every conceivable data set in, every conceivable environment. Or. You. Have to be stuck, with some imperfect. Analysis. Now. Having said that all. Analyses. Are chemical, analyses are imperfect. Even. Atomic. Absorption, which has the finest, of lines and you can do, a nickel analysis, and, you have one line that, you can pull out of the AAA spectrum, if. There's an interference, in there that produces, that line, you. Then will get an incorrect non. Validate, I mention the third part accuracy. Precision, and, validity you'll get an invalid, measurement, even, from an atomic absorption, measurement the. Question, is how, probable. Is that and. The. Answer is very, improbable. That's, a pretty specific line, in the, world of chemistry which is extremely, diverse, so. Constructing. The multivariate, data set to. Be probabilistic. Of any. Application. Data. Set is I, think the biggest challenge, that people, have, and the. Second challenge is if you have. Multi-dimensional sensors. A heterogeneous. Array, is better than a la jeunesse one typically, for information, content, but, if you have all, of these sensors and they. All have drift in n, dimensions, how. Do you correct for this drift and compensate. Or calibrate, so. The, people that say AI is the answer may. Be right but I, don't think it's going to be simple. There. Is another application that. Nanotechnology. Introduces. To this whole field of sensing, and that's. The ability to go from zero. Order measurements. To, first order and then second, order by, the time you get to third order, measurements. Then, you can introduce, that unknown. And you don't have to have all the testing, that, is, required, if you're working in zero, order or first order because. You're. And the. Examples. Of that in the laboratory are things like a GC, mass spec, because. Now, you've, got the, resolving. Power of the GC, in addition. To the mass spectrometer, that's already giving you multi. Information. And then you go and say or GC. Mass spec mass, spec that's. An example, of a third order instrument. Well, we can start to do that now with, nanotechnology. Because, we can put one type of separation. Device. That, may be very fast, and very small, in front of another. Type of sensor, and so, we can start, to, think about developing. Field. Applications. Of third order. Sensory. Devices that, can handle, real-world. Unknowns. The. Only thing I'd say about that is that everybody, wants, you. Know that I use sensors, that does, 100. Chemicals, or something DHS. Wants the hose, companies. Want out so everyone you deal with actually, once these sensor they think they're it's very easy you just plug him in and they work. The. The one thing that, we we. Deal with but. You. Thought you talked about it a little bit is, that. Sensor. Sensor interaction, you know so. We. Talked about some how your requires, low voltage the other one requires higher voltage, some, electrochemical, some community system and so forth and if, you put all those in same array, you. You you, would have noise, you'll have some, maybe. Thermal effect may be lurking effect and those, can lead to a lot of errors basically. So. So, that's actually my interest, part for, the bow, sensors, and totally, agree what's the the multi-dimensional integration. So for the sensor we're talking, right. Now it's not just a sensor itself actually is integrated. With sensor device so. It's not just the material, it's really the device fabrication combined. With the material, surface chemistry, and meanwhile with electronic. Integration, readout so it's a whole system so. I think, it's great pond that we can integrate, the sampling, process in, a combined, with the just the material, sensing part because, that sampling.

Integration. Can. Really. Give, you the much purer sample, you can target you and especially, for the barge example, that we're doing the biosensors, we always want to integrate, the, sample. Preparation in, just. The integrate. With, the sensing part so this really can purify, the, budget of some house mostly, they're really. Complex. Matrix, and for example the water or the foods and they. Already compacts. The matrix, and then we integrated. Standpoint process we can just, purify the target, we want to sense and. In that case and we have the much better improved, space at the city and a sensitivity, and that actually can solve the problem, for the the radar drifting, and also. The precision. Problem. As well. Our. Third, question which. Is very important. World activities it. Says can we detail, what are some of the needed standards. And materials, processes. Terminology. And things that, would be useful for integrating. That my small experience with United Technologies research. Is. That they have a group there that's interested, in sensors, like, for. Environment. And integrating, into architecture. So, it's part of the infrastructure, in the architecture, along, with carrier, conditioning, the air and, things, like that but, what. How. Do we interface some of the new nanotechnology. Materials and processes. And terminology. What standards, and kind of what. Kind of standards, would help get. Us to. Products. Faster. Very. Good so, I will I would like to say that we we have two, applications with, an ATC one. Of them is the airspace, application. And one of them is the building management, application. So in the building management industry, what we deliver, to. Residential. Buildings. Are smoke, alarms, and flame, and fire detectors, and, a. Lot, of what. We do right now is kind, of based on ionization. Ionizing. The air and then. You, measure a current as a baseline and then for example when. You have a smoke, the, smoke will reduce the amount of current you detect and therefore you would know that there's a smoke in your way and so on, so. What would work. For us is to, detect for, example all kinds of smokes we. Have a kind of it's more coming from from. Kind. Of material, that made out of wood, or plastic and, you will have different particle sizes and then. Right, now we are looking for nanotechnology. As a way to enable, small, false alarms, in smoke, detection and the. Way we do that is that we we, look into. Materials. That could be printed. That could be kind of made. Into conformal, and into flexible substrates and this. Material could be used as as iron, sources, or as, electron, sources, and, then. The standard we look for is more like if I, want to have nano, ionisers, if I want to have a material that can create. Ions, or creates electrons as a source for my organization, I want to have some requirements, on on, the. Material, in terms of, lifetime. In terms. Of amount. Of currents can handle without having any Joule heating effects, in it or degradation from, the amount of current we pass into it I want.

To Have kind, of requirements. On on. The. Stability of this nano material in ambient, conditions, or if there is kind, of, oxygen. That could surround the material and during, the operation, with that oxygen destroyin, and a material, it will still function and. Supposed to be. In. Terms of nano materials our, research. That has been exploring nano materials, for those, applications. And said. One major barrier. Is the lifetime, because. Nano, materials are small. And they. Have different properties in terms of heating. Effects in terms of melting. Effect. And so on all. Times we drive some, voltage, and current through them and we look, into them after a. Few. Weeks of testing and so when you find degradation, we find some chemistry, being activated within. The material and that's because of some impurities. And some of the polymers that are part of the formulations, so. The quality of the samples are critical, the, lifetime in operation, Varma is also critical for our application, and being able to deploy it and meet, the requirements of 10 years of operation because right now we have smoke. Alarm that can live 10 years in your house these. On radioactive materials, and, we. Are looking into green we, are looking to get technology looking to the place those radioactive materials with nano based ionizers, for example and, the requirements will be the same requirement, is going to be 10 years of lifetime, and and that's. Where we need some sort of improvement in the Nano material formulations. And and in the, purity of those samples. Summarize the panel or say something unique. Well. Actually I, do, want to. Standards. And so and, it kind of came up in some of the talks you like your sayings made that you have particular, things that UTC, wants to see and is. This, an issue you think for some sensor developers or you've got, this technology you made it in your lab to. What extent do you think most developers especially early, on actually, know the, standards, that their material, needs.

To Meet like do they understand, the conditions, in which it will be used and is that a big failing, point do you think like when you talk, an academic researcher about licensing, that technology, you defined I didn't know it had to last ten years I mean is this, an, issue that really it relates, to testing in a way people don't understand the environment in which the sensor will really be used so, they have to tense test and so a little, bit if maybe may, and I'm not could speak to developing, in the lab how aware are you of these issues and if, maybe some of the people who try to license, could see is that really a big issue because. Again part of the purpose we hope for this panel is to educate the. Early stage developers. Okay. Of. Course. But. I, you. Have to explore, and invent in. The lab but. Then of course when you get to the practical side you have to start considering other, things so it's best to be aware of everything but. Maybe the panel has a. Just. Mention, one example so so, basically. Like. What Sam has said is that you know they assume that like. Joe said they assume that you you, will deliver but then they weren't a lifetime and. They. Weren't a reliability. But. For example when we did an Air Force project for example they gave us two pages of specs you know including, the lifetime, including the. Sensitivity. Including, the, power requirement, including, all, of that you know so sometimes actually you, have a list now. Whether you can meet that list or not that's a different story but but, but you don't have a list and and companies actually they don't give you that specific, list usually they. Assume, you know what you're doing. And they only care about the big stuff but then when you deliver something that does not meet their specs that lets you know, so. Actually having a specs like that is very very important, and very very helpful because, it helps their assertions for example try, to to. Meet that and if, they're, if they're lacking and one of these specs these specs could be two pages you know and. Then they try to modify.

And Adjust for our case for example. One. Of the specs actually was not even the spec that I talked about which. Is the, signal. Strength. Was not even on the sheet because I don't think they they. Got to that point yet, you, know but we we read into it I said well nano amp i mean initially, our sensor, was only, like pea grams and, you have to have a shielded room to even get that signal you know so you couldn't do it I said well this is ridiculously, you can't have that you, have to redesign your sensor completely, and then, they got to narrow ten narrow apps I said no that's not good enough you know you have to go, to milliamps, and then. They designed sense or several times until they got to that point. So. Actually, having, specs either from companies or government. Agencies is very very useful I think. To. Be, honest. And the first point actually I didn't, really to, gather information so from, the laboratory. We, really have, that experiment, discouraged, validates. Like SEM, and TEM some. Like oh we, established. In the lab standards. Cannot to validate the material, we discover, we, fabricate. And, once, we feel that's the Valdai from our standing, points, and then we move forward every week that. Will be really useful information, where you can align, our protocol. With the standard, protocol. I. Think. Another area, that is frequently. Overlooked, by sensor, developers. Is sampling. And I think sampling, can make or break the, performance. Of a sensor. Indeed. Indeed. Any. Questions, from the group before. We break for. Yes. I. Heard. Comments about multivariate, analysis, and trying to use that in, a. Sense to capture, the formulated, properties, of the sensor itself. But. You're limited to the fact that you your data your, multivariate, analysis, and structure that evolves, from that is only based upon the data that you have and. You. Don't have any confidence necessarily, you can extrapolate that to other. Material, types unless you have, an. Appropriate calibration. Data so. How do you propose. To overcome, that if you apply like a machine. Learning or deep learning, the. The. System does the prediction, classifiers, for you you. Don't know if there's a right or not you don't know what they mean you know how they articulate, into other so. How do you have you proposed, or. What's what, do you see in terms of approaches, to try to, address. The issues, give me your given given you're dealing with a formulated, material, it's, one thing to look at the raw material, properties themselves but then. The, the formulation, itself endow. It with a whole different set of material of properties, sometimes, how. Do you bridge what's the view in terms of how you bridge that gap okay so before I defer.

To Susan, for this I'll, give you one example of a very successful, multivariate. Analysis, it, was the degradation of, olive oil and, the, experiment, was done they took a bunch of olive oils. Different. Grades from Italy, and from, Greece and they. Measured the degradation and, with, a mass spec and they, found out that the molecular, basis, for this was formation. Of known aldehyde, okay. Then. They took olive oils they spiked him with known aldehyde, all the different olive oils and they. Did, multivariate. Analysis, chemical sensors sniffing, this. Known aldehyde. Spiked. Samples. At different concentrations and, they, created a calibration, curve and then, they went back and validated, that and took olive oils and let them spoil and they fell exactly on the multivariate calibration. Curve that, one they understood, the molecular, basis they understood, the matrix, the samples, were from the same matrix or virtually the same matrix, although there is biodiversity. From, year-to-year in, olive oils it, was sufficient, to show that this. Multivariate. Analysis, technique worked for, spoiling, of olive oil now, the that's, a good model or an, example, of a successful, one but. You can see it's quite limited, when, you think about that in terms of you know ambient, air or something or, breath for, example, with thousands. Of chemicals, and Susan maybe you have a more. Detailed comment. That's. A perfect, example of, where electronic. Noses have been successful. Because. Those are very controlled. Experiments. Where we can control, the environment in which the sensor is working that's. A closed, environment. Where. Sensor. Arrays frequently. Run, into problems, is in the open, world environment and, that's, where you can't predict, every, interference. That they may run across and, we don't have enough money in the world to, test for every variation that, they may encounter that's. Where, we, need to rely. On. Nanotechnology. To help, us get beyond, zero order or first order sensors, and move, into second, and third order, so, that we have that more. Robust. Approach. That's, one, approach, the, other is sampling, a many. Times sampling. Can. Limit. The environment, in which your sensor has to work and. That's. A very powerful tool, as well prime example, of that is the IMS you. Ims. Is a. Instrument. That can be used in ambient air well. It doesn't like water many. Of the chemicals that. It's trying to detect when it clusters with water, gives. A different retention time, and. So. You you miss the detection of it well, if you can, limit. The sampling. Such, that you can reduce, the, the water, and they do that by circulating. Dry. Air within the instrument, then. You end up with a very powerful tool, so. Though. Or two approaches. That I would, think. That's. Also, the, reason why we're looking at this design theory, as a way, of overcoming some. Of these. Open. World. Problems. Thank. You, one. More I. Would. Like to submission. That from our experience. One. Way to address, the issue about the variant is to use multiple technologies. So, basically, when you think. About a sensor, sometimes you think, about one technology or one way of detecting something, like. Think, about the flame if I wanna take, a flame or a fire in a room with. The flame, has signatures, in, visible. In the IR and the UV, band, right and if, you stick with one technology for example a UV detector you can design one. For and print one for probably $7, right but. That evey that UV detector could have also. Responds to the Sun radiation right. And then you will think that you can measure fire when you are measuring the, summer edition salt therefore.

One Way to do it is to integrate another technology in the same platform so. Which, means you can add IR a texture right. And then, you can go even further and add different, technologies. At the same platform so a platform. That can integrate more than one sensing, technology. Not only that Turk knows but also think. About using optical. Medicine. To to. Semiconductor. Medicine to addition to for example resistive, addition to to. Radar or alive' technologies, in the same platform and you, have different ways in different ways, of measuring the same thing and you. Have validation, here, therefore from multiple, detection. Mechanisms. And that improves, the false. Alarm and makes your component. More more robust in the environment, or the application, so we do that a lot in the aerospace and in, the building management technologies, but we integrate more than one technology for the. Same sensing, principle. You. Probably won't be able to answer this what I'm asking anyways I'm into the Irvine I'm a program manager at DHS, science. And technology, and. So a lot of our customers which, are the DHS, components Coast, Guard TSA. Etc, they, don't really know what their requirements or. Needs are so. They do come to us and I think somebody mentioned wanting everything, for, no cost no false alarms yes we. Get a list that looks just like that so. In. Throughout, the talks you know I heard things sensitivity. Regeneration. Repeatability. If. You. Had, to kind of prioritize. Those. Characteristics. Would you be able to do that how would you do it what, should we be thinking about we being the, program managers, that are deciding, how to spend dollars to. Fulfill the needs of our customers what, how do we you, know think about these, sensors, and how to prioritize so, we can get some, kind of product out in the field. So. I'll ask the, panel to chime, in on this but. From my point of view I would, like to see agencies. Write performance. Specifications. Rather. Than. Characteristics. Of a sensor, so. For example, I'm, a fireman, and I want to run into a building and I need to know whether I'm gonna I need myself containing. Breathing suit, a. Requirement. For an activity I need. To inspect, train. Car derailment and chlorine, as offense. Build I got to have a simple chlorine device, somewhere. On my equipments. And I carry you. Know a rifle, a gun and, a, skubick, system. So. I would rather see the agencies. Write those. Kinds, of user, scenarios. And then. Ask. The, community, to say, let's, define some sensors. That approach, that or. Could maybe, in some way combine and get a, useful. Tool to, that person, so their. Health, is protected or asset. Is protected, or something like that. In. My experience. I believe sensor. To sensor reproducibility. Is. Very, very crucial because, you. Can't afford, to do all the testing that needs to be done on every. Single sensor, that's produced, you, have to be able to do the testing, and then. Hope that that, calibration, transfer. Moves. To. Every. Sensor, in that batch so, that all you're doing at that point is validation. Testing, I. Think. I think Joe said what I was gonna say basically, it has to take the user and the, sensor maker to actually need to come if. The user doesn't know then they have to get together and actually, decide what, what, what are the specifications because.

Only The user will will. Know the needs are you, know and the. Sensor designer, or, the sensor manufacturer. Will. Know what the limitations are and so, they can they can reach some compromise for, example, we. Run into a lot of people that ask. For a sensor that we. Cannot make for example you know and so, but but they, may actually have some kind of flexibility, in terms of knowing. Yes. Well I really would want you know 500 degrees Celsius but I can live with you hundred you, know say okay then we. Have something you know so. But. But, like a TSA or a situation fire, or chemical, spills and so forth only the people that actually deal with this will, know what they what they want what they need and then, the user can say well okay this, sensor that I can give you will last for six, months they. Say no that's no good I mean I want it for like five years I say, well you know we maybe. We can do it five years but we don't know yet so. For. The Air Force for example was our project, they they knew they gave us a very long set of specification, but, then in, our development, we found out other specification, that was not there and because, they didn't actually know. Because, they didn't have any sensors at that time they were actually funding, a new type, of sensors so, so, I think, what Joe you got right on the money because it's it's very important. It's. A human endeavor and it's very evolutionary and, to. Have the user. Define, on, a regular, basis, what their needs are in. Terms, of fit, form and function and, then, to have sensor, people describe, what, they can produce so. Maybe. 300, degrees C fits 90%, of the market you could do a lot of good you, might still want to do that even though when. You get the requirement, from the user, he, says that he needs 500, you know which covers all of his situations, but. Maybe 300, covers 90%, and that would be a tremendous, advance, and so, you get together and you decide what's critical, on, the other side you know United, Technologies knows, if, they don't have a 10 year sensor, they're not competitive, in the market and it will never go anywhere and so, that's a drop-dead, requirements, and so, you have dropdead, requirements, you have nice to have you have want to have and that dialogue. Must. Go on between. The user and the sensor. Developer, and. I. I always try not to draw hard and fast lines. I, think, that the different way to think about that question is we, can start with the problem identification. So. Here. They you don't know how to connect, what type of user with what type of sensitive, one but you can start with the problem identification, that's, really the problem in prison, in the real world and we need to solve this problem expensively, and that, can bring, you to, specific.

User And the developer, and developer, together. To, think about a solution to. Come our solution, to solve this problem, so that can be more efficiently. To facilitate. This communication. In the, to develop the sensor, that can be used, in the real world a. Comment. From you, know our experience as a small company trying to develop a product so as you. Know reflecting, on Joe's comment and everybody, said the same thing how, you develop, what's important, to have and what's, needed, is basically, you dialogue so the way we do it is and and, one thing is every, sensor application, and customer, is different in terms of needs and requirements, like I said one would be sixty, degree C is okay the other person it's ninety degree C is not nearly enough so. First. Step is sitting. Down you. Know together face to face and sort, of discussing what's needed and then, what, we can do and, other. Thing is it's, eventually. At the end of the day as a as, a manufacturer, as a business, you, know running, the company, you have to make a decision, whether you, actually. Want to pursue down that business, line to address, those markets. Because sometimes. Specification. Czar very market, oriented and the application, like. Automotive. Sensors' have, very different specifications. Versus, your smart home sensors, and then, if you're trying to address a wearable markets, that we saw on the slide, packaging. Requirements are. I mean out of this world I mean, because guess, what you, don't care you you don't use your cell phone for ten years you'll, be like lucky, if you use it for one year so they like lifetime. Is important. But guess what's more important, the packaging, size form, factor I mean you sit down with those people that, makes like, these smartphones when, they talk about the size and the cost you're like wow it's very different ballgame, versus. Like. A thermostat, or a smoke alarm like you know you know it's ten years lifetime, so, it's very different ballgame, every, single application every, single you, know market, so, it's better to understand. Which market, you're gonna be focusing, on as a sensor manufacturer, and then, just you know see if you can address that, exactly. What's needed by your. End users, and customers and, it starts with the dialog and you have to reiterate back, and forth on that for for awhile yeah. I would just repeat. Almost. What most. Of the panelists. Said I think. The key success, for the requirement. Document, to be released to you early is its kind of communication. And be. Involved with the end user early. On so if you want to be successful and. Align with their need, and meet, the requirements I would suggest that you try, to know. Who your target. Application. Is and. You understand, who's a player in that domain, or that space talk. To them understand, requirements, as much as you can and then you kind of make. Your, material. Or design, or sensor, aligned with their application. And then when. Talk about requirement, account is really an application. Document. Right it comes from the application, so when that document is created you, have in it operation, requirement, things, that come from the. Operation, environment, itself how long is going to last and what kind of temperatures needs to arrive and amount of pressure the amount of vibration. And so on and there. Is also the voice, of customer kind. Of wish list right you have a customer who would tell you Oh III, ever, since that can meet, other comments it does everything I need but I need to do this thing as well, or additionally, measure that thing or or the. Lifetime being a little bit longer or can be seamless. In my component, not visible it doesn't obstruct any airflow, and so on so, there are things that can as just, hit must-haves and things. Called wish, I mean desire, to to, have in your system or your sensor and understanding. Both of them is it critical for, your kind of engagement, and and and success in being. A supplier for an. End-user. Additionally. On the materials, the materials are, I would say the key element. In the sense of technology every sensor almost has a material in it and understanding, what.

Requirements In the material there is is important, because materials, are as you know can. Degrade can, change over time and can response a lot to the application, salt so. Again. Back to the human, element. For me and. An honest conversation between. The, user and the developer. And the people in between on, what. Is a. Drop-dead. Specification. A must, have and what to wanna have and. And it, in in my experience. It's, also a timeline, in, other words, if. You ask me is it possible, to develop nano, sensors. In AI, sufficiently. To do a lot of. Multivariate. Sensing. Nanotubes. Sampling, systems, have, the whole integrated, package I would, say to you yes and there's many. People out there are developing, different parts of this all the time the, technology is moving quite. Rapidly. But. If you want this in a package, for the field tomorrow if, I told you I could do this I would, probably be, overstating. It I don't, use the word, fib. But I would, be overstating. It, would be super. High risk if you wanted to think of it that way and if, you but if you absolutely need, it today and need to make an impact then, maybe you have to make compromises the, package is bigger the. Power level. Is higher you. Need more sampling, more macroscopic. Things, built, around your little nanosensor. Of today. And and. So then you look at well okay. Do I go with a, cost, of that's. Undesirable, and a size that's undesirable but I get it quick or. Do. I you. Know hold out for everything and I. Have. A paradigm, that says. Fast. Good, and cheap pick. Any tool. And. If you think about it the. It translates, really. Into, that kind of world many times so. I want to go out to one more question or, you want to. So. So this is actually the sensor that I showed in my presentation, about the chemical sensor and and, this. Consortium that funds these projects. Had. A workshop. To just define what they want and, so I couldn't go so I sent one of the young professors. There and he, came back and he says there's, no hope we're, not gonna submit anything I said what he said they wanted a sensor that's, that. Can detect chemicals, and that has electronics, and they, wanted the size to be between 50 and 70 nanometers. And. He said that's impossible when. I got to something anything I said no no we're gonna submit he says no no you cannot it cannot be made I said I know it cannot be made. But. But these guys don't know what they're talking about. So. We submitted a proposal, and-and-and. We propose. Something between 100. Microns, and 500, micron and guess what I think we proposal. Had the smallest size proposed. So. That that's the case where. The. User wants. Something but but they don't really know exactly what, they need and these guys the reason they wanted 50 to 70 nanometers because they wanted to go in the pores of the rocks the. Test stuff. Yeah. So many. Stories are written in The Naked City. So. Any more questions I think there was one more out. In the audience. No. I I have, one more, okay. We. Talked about, nanotechnology. And you know materials. Integrating. Them into sensors, and then, integrating, the sensors, into, electronics. And then, packages.

And. Instrumentation. Or providing, information, and the, question to the panel is do. Self-contained. Devices. Provide. Greater, design, freedom greater paths to market, than. You. Know just nanomaterials. And sensors. Yeah. I think this. Is a really, good. From. You know from n 5 give. You a little bit of background, so. What, we are trying to do is, take, the chemical censorship, which is an analog product, and we, sell that as a product, itself, combine. That with a something. Called ASIC which is an application-specific, integrated, circuit, and. Some, other components. Put. It in a small package with a filter membrane that, would you, know selectively, remove some components, and particulate, matter everything, in, a package, so. Size. Of like 3 by 3 millimeter and about. A millimeter take the reason for that is that's the wearables, liking I mentioned. Before that's kind of the size form, factor requirement, the, reason you're trying to do that is a, self-contained. Sense. Is where, everybody, is going if you're trying to address the. IOT, or wearables. Industrial, IOT smart homes this. Is what. You want because in this, day and age software. Still, makes the products, profitable, and as, the end of the day everybody. Wants, data, I mean nobody really cares, about sensors. You know like, it can look at a black box but as long as it provides the data and people. Like, the app developers, and, and. You on top of that cloud computing you. Add on on top of that and it gives you actionable. Intelligence, that you can do this and then you can add on top of that make, some services, products, things like that so, putting it all together is, a significant. Challenge although, this is probably, the. Easiest way, to get to the market and grow, your company rapidly. And, you have many examples like, that from companies. Like Centurion, that, produces. An industry, standard temperature. And humidity, sensor in a single packaged all digital, product, so you drop it in any board and it just provides a solution what you need anybody. With like. You, know maker, type education, like, twin tinkering, with like, boards, Arduino stuff they can integrate into their product and may actually make a product that's why you see in the crowdfunding campaign. More and more hardware, based, products, are coming in because, now sensors. If you look at MEMS. Sensors, everything, this is where they're moving to all digital. Solution, like a single package but. There, is a problem, is that, developing. That kind of sensor is extremely. Challenging and, time, consuming and resource intensive, for a start-up, like our company, because guess, what you have to not only develop the sensor, you have to pay for the ASIC development, you have to pay for the packaging, development, you have to line. Up every, single thing so there are like you know, kind. Of both ways goes both ways that's the easiest way to market, and this is probably what the world, needs right now but, it's not that you, know the easiest, produce. And kind of manufacture, at a large scale, but. I think I would shape the main point really. Rate. Is if. You. Have the whole package and useful, data nobody. Cares about you, know necessarily the, the, interaction, of the sensor and the signal from the sensor but, temperatures, humidity compensated.

Output. Then. You can go to all of the engineers, making products, and. This. Is expensive and a generic ASIC doesn't exist for, all. Sensors, in all sensor types, we. Have a couple that are coming out for like your chemistry the TI, they. Have one now and they're coming up with a next generation with temperature humidity on, it and a. DI analog, devices is coming out with one so. People. Are starting to move forward but until, the investment, is made like. The investment, in. Semiconductor. Devices op-amps. Computers, microprocessors. And, you see from the 1980s. The first RCA. 1802. That we used in our sensor, arrays which, had two K of memory in, Ohio. At, all to, what you can get today for $2, in. Terms of micro, processors, with A to D with IO, with, temperature, with all kinds of features, on them. I think. Sensors, have to be evolutionary, and we, have to have platforms, that develop, that. Also support, the, sensor infrastructure. In. Order to connect to the marketplace to engineers, who are doing devices. You, know garage door openers, and. Controls. For appliances. And, all kinds of interesting. Machines, like that. Other. Comments I would like to make, a comment on the. Question I would. Say that the. Machine. With a self-sustained. Or integrated, sensor is an, application, driven, decision, I would. Like, to point that for. Aerospace, technologies. Aerospace, components that, are really high-end and expensive something. Could be as expensive as $20,000, point, 2 million point, 2 million dollars worth. Of a component what. We had been looking at into, in terms of sensing is to integrate and embed sensors, within the component, so. Those kind of applications, nations require. Sensor to be just embedded and seamless, in existence. That, are really, causing no harm to, your structure. Design and and. The best technology, for this is really printing, and nano combined, together so you really marry, nanomaterials. With printing technologies, and this, gives you an option to really print, things. You can see by eye, inside. A composite and that, could be a strain gauge could be RTD it could be an antenna. And this. Is part of your manufacturing, platform right now it's not really something that kind of stand-alone or something, that can sustain will it's an electronic. Circuit. Or front electronics circuit it's, more like you just need to print that design, at, that, specific location with. The minimum amount of material, or mass, added, and without any harm, to your Aero design or a structural design and being, able to wire or wirelessly, interrogate, it in, in. The component and then that. Application. Requires, just really a focus on the integration and and, what kind of manufacturing I need or is, available, that enables, me to print, a strain gauge that's. Kind of, 100. Micron behind micron in size for. Example and and. That strain gauge is rededicated, to measure the, amount of load and I'm going to damage that, could have been to an aberration, of a rotation of a fan blade right, yeah. For ardent for me one of the major things is to have this dialog, and maybe. There can be some. Instigation. Of these dialogues, by NSF. Manufacturing. Because. I think you can play an important, role bringing. Industry. In, sensor. Developers, together. I. This. This, whole thing is. It's. Really part of a, very important, dialogue that. You have to have because. You. Know you can spend lots of time and money developing a specification or, a. Manufacturing. Process that's totally, irrelevant to. A. Vertical and, every vertical whether you know a bishop pointed, out automotive, man. These guys are virtually. Impossible to deal with for small companies they. Need to supplier manufacturers. Assurances. Certifications. Up the wazoo I mean it's just crazy so. For a small guy these are difficult so, it's going to take some partnerships, from, market verticals to have. A discussion, around those with sensor manufacturers. To, integrate, these. Skill. Sets that. Are needed to get the nano technology to, manufacturing. To actually, verticals, and they're all different whether. You want aerospace, automotive. Industrial. Very. Different again a whole different. Thing but some of the major topics, in, the major market, areas could. Be instigated, by NSF, and by the way I must say, that in my experience with NSF, it, was one of the few agencies that. Followed, the technology. And, let. The group develop, we had funding for about four years from NSF, and now.

We Have a standalone, company and they, did that with a lot of the centers they, had initiated. Funding, and then, the center's would have five years and then standalone and, and this has proven to be a really successful. Way. To fund, technology, and agencies. That have. Unsecured. Funding, and it changes, from year. To year and this, is not necessarily the problem of the program managers they're they're. In a very volatile, environment, but, they have a much harder time to, get some of this mission science. Done, and. I think it would be better if they were if they had Corps funded, efforts. That were more more, secure, because. You developed, a group you develop the expertise, you develop the interaction, you develop the supply chain and all the relationships that you need to do a product, which, is non-trivial, it takes a long time. Relatively. Few of us are probably interested. In this but so my question would be to, each of you is is there a particular federal. Program, or agency that was really, helpful to. You and your, business. And is, there a particular federal program that was. You know sort of got, in the way. But. And sure, yeah. No, no but up here I'm interesting. You know I'm interested in the good in the bad I'm just curious but like not long diatribes, about you know paying taxes or something like that. So. III think a federal, program that I would like to point to is the next flicks Institute, this is recent, manufacturing. Student in California in San Jose funded by the DoD and the reason, we like, it because membership-based, they're, trying to bring together industry, and academic, members work. Together and, what. Was unique about it is that the roadmaps for the project were made, by the group Mumbai, then sit members so basically it's kind of road map driven, and the, road map is created and and made by the members themselves days, and their needs and vision, for their technologies, right this is one thing the second thing is that and, their proposals, you need to have kind, of team members from different, organizations. That, requires. Academic. Staff as well as industry, staff, and they also, specify. The TRL. Level for, the proposal so they wanna they have a clear description. Of what we're. In that er all material map they want to in make the investment, and with technology they want to move forward kind. Of up into the commerical, line, and, additionally. The. The, kind of provide, feedback. Through. The nozzle. Funding, they provide details, on why and why it has been studied and so on so they've kind of very engaging and cooperative. Institute. Yeah. For, n five of course you know you, all know the SBIR, has been in a great support like Angela's, program. And then from NASA also, but. One program, it's. Not really, a program but in. Terms of infrastructure, of course you know like from nest the CNS. D Nanofab, and I. Think, NIST was one of the facilities, that had, this, kind of like like. An architecture, which allows, like. A very easy access, for the small businesses, to come in with, our team of engineers, and start building, our own like. A manufacturing. Line and, R&D line inside, the federal facility, of course paying for it and after that ARL, had the open campus so I think opening. Up these really. State-of-the-art like. The facilities that of course you know taxpayers, dollars went in and building, it up but actually. Fostering. An ecosystem, around this local area where, small businesses, come in and develop, a product and then of course getting. To the manufacturing, through contract manufacturers. Things like that and it, just helps more, small businesses, because you see a model, that's been you, know it play and. Successfully. We have developed. Couple of products working at nest so. I think that has been a great help four and five and, yeah. It's it's going, on and we see more and more, federal. Organizations. Are actually opening, up, not. Only like you. Know just a manufacturing, but also, characterization. Because that's one thing that small, businesses, cannot afford especially.

You Know materials characterization, so. From. The standing point of the academia. Like, the. Small business, stops mail from the University the ASF our crops it's pretty helpful and we say and because, they provide, the program and the constants and they help to bridge the gaps between academia, and industry, and help, the faculty. To identify the, partnership. And also the problem and the, reframe. The product, developments, in that. Program, actually. Provides, a lot of critical, questions, for you to think about the prototype development, in that, way actually when, you're stating academia. Were saying it differently, and you, can hear a different perspective who helped you to develop, the product and. Also a, girl. Funding from the USDA nano technology. Program as well so this is pretty helpful to, drive, the innovation, to, try. To have the innovative, technology, applied, to. The particular real, world problem, too so. We. Had good experience with an SF actually very good, hundred, hours center and like. Joe said the. Long-term funding actually is very very important because we would have not been able to develop this technology had, we had a two-year contract or, or so that was actually very very, essential. We. Have not had I'm, glad that Sam had a good experience with next flex we have not had that I. Concept. Is very good but the way. That. And many these. Institute's. Operates, is that the more money you pay the. More say you have in the topic or the call the, more money you pay the. More say you have and which projects, get funded and, if. You're a university and you can't get your propose to pay the high fee then. You don't have actually, much say in and, what what is decided and so forth but. The concept is excellent, actually I mean, just. I I just have a little bit trouble with their fee, based system, there are actually other Institute's, that do not require as much. Membership. Fee yeah and there are some that require more actually membership fees so that's, only trouble I had but the concept actually I think is very very good it's. Actually, more like the Fraunhofer Institute. I'm. In a unique position here since I am government, but. We, do get our funding. From other government, agencies, and sustained. Funding is, the, key and my. Two, best sponsors, over my career have been NASA and, DHS. And. Those. It's primarily, because they, had the long-term, view and allowed. Us time, to develop the. Technology. And move things forward. Okay. I think, you, know government programs, like government. Industry partnerships, government. University, partnerships. And, National. Lab partnerships. I think all of those are part of the technical, infrastructure. Of. Our country and bringing things forward I'll. Tell you one other thing the Small Business Administration and, SBIR. SBIR. Is really, the, technical part of SBA he had, the way I look at it and SP. The small business have a one. Program, or many programs that one that I used was, score the, Service Corps of retired executives. My. First company I had an SBIR, and I sold some instruments. To Finland, and, I said could God how do I do this. Import/export. Customs. Invoicing. And all that and the, Service Corps of retired executives. At SBA called him up and I said I need a guy who. Sold, stuff, to Finland, you know and shift, it there and they found a guy an ex executive, from Motorola and he said hey, you need a freight forwarder he, does all this work you need a bank and a letter of credit they, do all that work and it's, in and out there's no problem and and, I mean it was amazingly, helpful. And. Interesting at the same time but, there are a lot of programs. Within SBA, that. Are helpful to technology. Companies, and the. SBIR, for, startups, and innovation, is tremendous, and then, as you get closer to products, that the. Industry. Government. Relationships. Are more important, for, innovation I think the government, University. Relationships, are much more important, and the National Lab system is absolutely. A gem, in this country, and needs. To be.

Cherished. And resource. More. Than it than it is for, the, mission that we have to. Fix big problems, like our, health and climate change and and. All these sorts of things that I include the the, naval research labs and the big labs didn't with the National that's their, tremendous resource, for us. US. Patent. They. Have a small entity filing, status that's a little less egregious, but it's but. The patent system is still something. To navigate, unless. You've got experience. 100. Patents and, on. Average they take about four years five. Years even. If it's completely new that there's no that. Those will take three years if there's it's new there's no nothing like it for example it. Will go fast but. But if there's something similar or just even keyword search, similar, it, will take five years at least. So. What I do want to point, out to everybody is, I mentioned it before but this map right here there, are a lot of options around Arlington so, you should be spoiled for choice to eat and, if we can get, back here I think that it's 140, is. That we'll be convening, back so let's try to do it on time so. That we can end the day on time but, that, was really a great panel thank you so much I really I thought, that was really terrific thank, you.

2017-12-10 18:32

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