Test system, and test method of device under test

Abstract

To provide a test system that can reduce the temperature cycle time, and the switching out of devices in the oven, to virtually zero time.SOLUTION: A test system 10 has: a test and measurement instrument 52; ovens 20-26 that hold devices under test (DUT); oven switches 60-66 that are selectably connected to the DUTs; channel switches 70-76 that are selectably connected to the oven switches 60-66, and to one channel of the test measurement instrument 52; and one or more processors. Therein the one or more processors are configured to: select the oven and the oven switch; connect the oven switch to a subset of the DUTs in the oven; connect the channel switch to the oven switch to receive a signals from the subset of the DUTs, and send the signals to the channels of the test measurement to acquire waveforms in parallel; use a machine learning system 14 to tune each DUT; and test whether each DUT in the oven is optimally tuned.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] FIELD OF THE DISCLOSURE The present disclosure relates to test and measurement systems, and more particularly to a test system and method for adjusting and testing parameters of a device under test (DUT). [Background technology]

[0002] Machine learning techniques can be used to reduce test times for tuning parameters of a device under test (DUT). U.S. Patent Nos. 11,923,895 and 11,923,896, issued March 5, 2024, and incorporated herein by reference in their entireties, disclose systems and methods for tuning parameters of DUTs, such as optical transceivers. These systems and methods can improve test times for tuning optical transceivers and other DUTs, for example in manufacturing environments. These systems and methods can reduce the tuning parameter test time for optical transceivers, which optimally tunes and predicts and verifies optical parameter sets, from a worst-case conventional 2 hours per DUT for one temperature to approximately 12 seconds per DUT. This represents a significant speedup compared to the conventional worst-case tuning process.

[0003] U.S. Patent Application Publication No. 2023 / 0314498, filed March 24, 2023 (hereinafter the "'498 application," the entire contents of which are incorporated by reference into this disclosure), describes a test system that uses parallel pipeline ovens, where multiple transmitter output signals from each oven are processed serially, one channel of an oscilloscope at a time. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] U.S. Pat. No. 1,192,3895 [Patent Document 2] U.S. Pat. No. 1,192,3896 [Patent Document 3] US Patent Application Publication No. 2023 / 0314498 [Non-patent literature]

[0005] [Non-Patent Document 1] "Tektronix oscilloscope" introduction site, Tektronix, [online], [searched July 8, 2024], Internet<https: / / www.tek.com / ja / products / oscilloscopes> [Non-Patent Document 2] "Optical switch" introduction site, LuxLink, USA, [online], [searched July 8, 2024], Internet<https: / / www.luxlink.com / products / optical_switch.htm> Summary of the Invention [Problem to be solved by the invention]

[0006] However, the DUT conditioning process may include conditioning to various temperatures. The time required to ramp the DUT up and down to each desired conditioning temperature results in significant delays. For example, in one conditioning process, each ramp time is 180 seconds. Furthermore, additional time is required to move the DUT in and out of the temperature chamber or oven used for testing. Thus, there is a need for an overall system design and method that can reduce temperature cycling time and shorten the switching time of devices in the temperature chamber or oven to essentially zero. [Means for solving the problem]

[0007] The embodiments of the present disclosure address the problem of reducing temperature (Temp) cycle times in testing a DUT, and in combination with a machine learning (ML) system, reduce the overall time to test a DUT. The embodiments of the present disclosure provide systems and methods where the total tuning time is generally dependent only on one channel of an oscilloscope and the amount of time the machine learning takes per transmitter. These systems and methods provide fully tuned transmitters at multiple temperatures. In one embodiment, a three temperature system can run at a rate of 36 seconds per transmitter without interruption, which represents a 200 times speedup compared to the worst case tuning time of 2 hours for a traditional tuning process.

[0008] The embodiments of the present disclosure use novel techniques for pipelined data processing, novel machine learning elements, and novel techniques for serially using separate channels of an instrument (such as an oscilloscope ("scope") for separate temperature chambers or ovens. The embodiments of the present disclosure do not parallelize the acquisition of multiple channels of an oscilloscope to achieve the roughly 200x speedup. Rather, the embodiments of the present disclosure process serially process multiple channels, one channel at a time. This avoids the very expensive and time-consuming process of redesigning the oscilloscope hardware and software to accommodate channel parallelism, since no channel parallelism is required. Another overall advantage of the test setup configuration according to the embodiments of the present disclosure is that it minimizes the number of layers of optical or other switches required in the signal path between the DUT and the oscilloscope to only one layer. This has the advantage of reducing costs and improving signal integrity compared to current manufacturing systems where four waveform inputs are manufactured to be processed simultaneously in parallel by four channels of the oscilloscope.

[0009] This embodiment employs a parallel pipeline architecture. The number of pipelines is equal to the number of ovens or temperature chambers used. The number of pipelines also depends on the number of switches connected to the DUTs in each oven to cycle through the multiple DUTs, the size of the switches connected to each oven to select among them, and the number of channels of the oscilloscope or test measurement equipment. Another aspect is the number of DUTs per oven. Each of the oven's DUT switches connected to the multiple DUTs in the oven has a number of switches equal to the number of DUTs in an oven. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 shows an example of a test system using a pipeline. [Diagram 2] FIG. 2 shows a pipeline diagram of one embodiment of a test system. [Diagram 3] Figure 3 shows the timing relationships between the four pipelines. [Figure 4] FIG. 4 shows the timing alignment of an embodiment of four pipelines for four channels of a test and measurement instrument. [Diagram 5] FIG. 5 shows an example test system using machine learning optical tuning. [Figure 6] FIG. 6 shows an example of a single-pole double-throw optical switch. [Figure 7] FIG. 7 shows an example of a dual single-pole, double-throw optical switch. [Figure 8] FIG. 8 shows a parallel pipelined embodiment using two ovens, 32 transmitters and four channels per oven. [Figure 9] FIG. 9 shows an embodiment of pipelined parallel processing using four channels of parallel test measurement acquisition. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] The following description uses a specific number of ovens and oven DUT switches, a specific size of test and measurement equipment switches, and a specific number of channels on the test and measurement equipment. These numbers are for ease of explanation and understanding and are not intended to limit the number of components in the system.

[0012] The embodiment of FIG. 1 illustrates a test system that uses a pipeline 10 under the control of a customer test automation application 12 and a machine learning system 14. The customer test automation application 12 and the machine learning system 14 may both reside on the same computing device 16 (which may have one or more processors) or may be distributed across multiple computing devices, each having one or more processors. The customer test automation application 12 may control the switches through a programmatic interface (PI). Similarly, the test automation application may communicate with test and measurement equipment and the machine learning system through the programmatic interface.

[0013] In one embodiment, four ovens 20, 22, 24, and 26 are used. This means that there are four pipelines (four oven DUT switches 30, 32, 34, and 36, and a 4×1 device switch 50 connecting each oven DUT switch to the oscilloscope clock CLK). The transmitter Tx needs to be adjusted to three or more temperatures, and each temperature takes 180 seconds to ramp up. To take advantage of the parallel architecture, an embodiment of the disclosed technology overlaps the ramp-up time of each oven temperature change. As a result, the oven ramp-up time is effectively 0 seconds. Similarly, the time to move the transmitter in and out of the oven is also effectively 0 seconds. Each pipeline also contains a serial operation task.

[0014] In this embodiment, each oven contains eight transmitters that increase the temperature. The DUTs in this embodiment consist of optical transmitters, but any type of tunable DUT, optical or electronic, may use this system. An "electronic" DUT is a DUT that is not an optical device. When eight transmitters are used per oven, each of the oven DUT switches 30, 32, 34, and 36 results in an 8x1 type switch. In the case of optical transmitters, these switches are optical switches.

[0015] The eight transmitters in one oven are heated in parallel, but the eight transmitters are sequentially tuned one at a time to one channel of the test and measurement equipment, allowing parallel processing of all multiple pipelines while avoiding costly redesign of the test and measurement equipment.

[0016] Each of the oven DUT switches may be connected to a splitter that splits the signal between the test and measurement equipment switch 50 and a channel on the test and measurement equipment 52. Splitters 40, 42, 4 and 46 pick up a portion of the signal from each of the four oven DUT switch output signals and apply it to a 4x1 test and measurement equipment switch that outputs it to the clock CLK input of a sampling oscilloscope so that the oscilloscope can perform clock recovery in order to acquire the signal on each channel.

[0017] The test and measurement equipment switch 50 selects the appropriate one of the 8x1 oven DUT switch output signals to condition the transmitters in the oven that have been brought up to the correct temperature, and the selected 8x1 DUT switch cycles through all eight transmitters Tx in its corresponding oven.

[0018] During operation, the test and measurement equipment 52 acquires waveforms from the transmitters. One aspect of the system is that only one channel at a time is processed in a serial manner. Multiple channels are not processed in parallel because only one channel at a time is ramped to the correct channel. For example, when the oven 20 reaches a certain temperature, channel 1 sequentially acquires waveforms from each of the eight transmitters in the oven 20 at that oven temperature. The user's test automation application 12 passes these waveforms to the Tektronix tuning algorithm 14 to train the deep learning network and predict optimal tuning parameters and compute TDECQ validation of the resulting tuning. In other words, the time to tune the transmitter depends only on the oscilloscope acquisition time and the DSP processing time of the optical tuning application. The Tektronix optical tuning algorithm or machine learning system uses machine learning to provide tuning parameters for the optical transceiver based on the waveforms received from the DUT.

[0019] The customer application 12 acts as the main controller for the entire system. It is responsible for the timing and sequence of all tasks for all four parallel pipelines. These tasks include setting the oven temperature. The controller may pause while an operator or robot moves the transmitter in and out of the oven, and may also control the robot, if one is used. Tasks may also include loading tuning parameters into the transmitter and controlling an oscilloscope to acquire waveforms from the transmitter. The controller also collects waveforms and parameters and sends them to the light tuning application to train its deep learning network, and receives waveform measurements to verify tuning. Control of the system is realized in one or more processors configured to execute programs for operating various aspects of the system. The one or more processors may be located on a computing device on which the test automation application 12 is running, separate from the test and measurement equipment and machine learning systems. The test and measurement instrument and the machine learning system may each have their own processor, or all of these processors may be in one system, or any suitable mix of the two.

[0020] The controller also controls the 8×1 oven DUT switch to select which transmitter in the oven to acquire the waveform from and controls the 4×1 equipment switch to select the appropriate output of the 8×1 switch to apply to the oscilloscope clock recovery input CLK. The controller also controls the test and measurement equipment to acquire the waveform from the appropriate channel of the test and measurement equipment depending on which channel has the oven currently ramping up to temperature for the calibration operation. As can be seen with reference to FIG. 2, there is a period of non-use (96 seconds in this embodiment) between the ramping up of each oven and the calibration interval between the transmitters in that oven at that temperature.

[0021] As mentioned above, the tuning process employs a novel machine learning system that is trained to associate a set of tuning parameters for each DUT based on the waveform. This process may be repeated until the DUT is deemed to have either passed or failed. The machine learning system accelerates the cycle of tuning the DUT and testing the DUT to determine pass or fail operation. The test process includes a measurement process that also relies on the machine learning system. The system has been demonstrated to reduce the tuning time to 12 seconds per transmitter for one temperature. Because the ramp time for each oven is reduced to essentially zero, the system can achieve a tuning interval of 36 seconds per transmitter at three temperatures.

[0022] FIG. 2 shows one of the pipelines, and FIG. 3 shows four parallel pipelines, each served by one oven. In FIG. 2, oven 1, which has tuned a set of transmitters, receives a new Tx set at 160. At 162, the oven ramps up to a first temperature (Temp1), which takes 180 seconds. The transmitters are then tuned and tested at 164 using a machine learning system, which takes 12 seconds per transmitter, or 96 seconds for a set of 8 tested at that temperature. Once tuning and testing is complete, the oven ramps up to a second temperature (Temp2). There is an unused time interval of 96 seconds at 166. The process then continues with tuning and testing, ramping up to a third temperature (Temp3), another unused time interval of 96 seconds, and a final tuning and testing at 168. FIG. 2 shows only one oven and one pipeline diagram. The efficiencies and advantages of the pipelined approach come from combining multiple pipelines as shown in Figure 3.

[0023] In Figure 3, we can see that the four ovens provide a parallel pipeline. The top pipeline is the pipeline shown in Figure 2. More pipelines can be added by adding additional ovens. The process is as the first oven loads, then the second oven loads. As mentioned above, the tuning time for each transmitter takes 12 seconds. This breaks down into 2 seconds per acquisition for the four waveforms, 1 second for the machine learning DSP to predict the tuning parameters, 2 seconds to calculate the transmitter dispersion eye closure quaternary (TDECQ), which is the measurement used to determine pass / fail, and 1 second for margin per transmitter for a total of 12 seconds. Each test and tune block performs the calculations when the other three ovens are at different stages of ramping up.

[0024] After each test and adjustment block, there is another block for that pipeline to ramp up to the next temperature. After each pipeline has calculated the adjustments at each temperature, it is ready to unload the eight transmitters in its oven, then load a new set of eight transmitters and repeat the cycle. The unused time intervals allow the system to perform other operations that may not be covered by the light adjustment algorithm. Pipeline sequencing times may be adjusted as needed.

[0025] A parallel pipeline like the one shown in Figure 3 would provide a system that outputs fully aligned transmitters at an average ideal rate of one transmitter every 36 seconds. This given alignment time represents the ideal, assuming the machine learning predictions are 100% accurate. The 96 second overlap margin allows any inaccuracies to be covered to some or all extent. Even with these built into the intervals, the actual results are expected to be many times faster than current methods used on the manufacturing line.

[0026] As mentioned above, although this embodiment may have been described with respect to tuning an optical transmitter, this pipeline architecture and temperature testing may be applied to a wide variety of DUTs, either optical or electronic.

[0027] Thus, the present embodiment combines the optical tuning machine learning system described above with a novel parallel pipeline and test and measurement instrument channel switching architecture to virtually eliminate oven cycle time, thereby accelerating the prediction of optimized tuning parameters through machine learning. The present embodiment improves this tuning speed by nearly 200 times and achieves this without the need for parallel test and measurement instrument acquisition channels.

[0028] In other embodiments, multiple channels of the test and measurement instrument can acquire waveforms in parallel from the DUT. The following description focuses on specific features of the DUT, oven, and switch for ease of explanation and understanding. More general embodiments follow the initial description.

[0029] This embodiment distributes the output signals from the four transmitters in each oven simultaneously to four channels of the test and measurement equipment for parallel acquisition by the test and measurement equipment. This requires an additional layer of optical switches. These embodiments rely on multiple acquisition channels, resulting in acquisition times of 2-3 seconds, which is the largest portion of the time used to perform tuning and calculate TDECQ and other measurements with the help of machine learning. A test system according to an embodiment of the '498 application that uses oscilloscope channels in series achieves a nominal 75x speedup of machine learning tuning parameter predictions compared to a customer's typical tuning time. This embodiment provides a 320x speedup of the customer's worst case original 2 hour tuning time.

[0030] FIG 4 illustrates an example sequence of test operations according to an embodiment of the present disclosure, and FIG 5 illustrates a block diagram of an exemplary test system according to an embodiment of the present disclosure. The system is novel in how it pipelines the processing and has a novel machine learning element to predict tuning parameters for the DUT transmitter. Also novel is the addition of a second layer of switches to allow acquisition of four channels at once on the oscilloscope, greatly speeding up the tuning process.

[0031] With reference to Figures 4 and 5, various embodiments of the present disclosure may be configured with a combination of one or more of the following aspects: The parallel pipeline architecture shown in Figure 5 has four ovens. Each oven constitutes one pipeline, so the four ovens correspond to four pipelines. In this system, the DUT may be configured with an optical transmitter and is tuned at three or more temperatures. As will be explained in more detail in relation to Figure 9, the temperature ramp affects the timing of each pipeline. In the embodiment of Figures 4 and 5, each oven takes 180 seconds to ramp up to a given temperature. Thus, the embodiment of the present disclosure overlaps these ramp times for the temperature changes in each oven. This results in an oven ramp time that is essentially 0 seconds. Similarly, the time to move the transmitter in and out of the oven is also essentially 0 seconds. Each pipeline also includes a DSP task for serial operation in the oven until the temperature ramps up.

[0032] In the embodiment of Figures 4 and 5, each of the ovens 20, 22, 24, and 26 includes eight transmitters that ramp up in temperature, which represents another aspect of the parallelism of the architecture. As mentioned above, each oven provides one of multiple serial pipelines, so the embodiment of Figures 4 and 5 has four serial pipelines in the architecture. Each oven cycles through three temperature settings. It should be noted that embodiments of the disclosed technology may be configured for more or fewer ovens, more or fewer transmitters per oven, and more than one temperature. Three temperatures are the most likely nominal requirements by manufacturing line customers. Furthermore, the term "oven" should be understood to generally mean a controlled temperature chamber. Thus, embodiments of the disclosed technology may control the temperature of the oven below ambient temperature to cool the DUT, if necessary.

[0033] In the particular embodiment shown in Figure 5, switches 60, 62, 64 and 66 each include four single-pole double-throw optical switches or two double-pole double-throw optical switches, one for each oven. For the purposes of this description, these will be referred to as the "oven switches." These comprise the first layer of optical switches that select four of the eight transmitters in the oven that are increasing in temperature. These four waveforms are input to the second layer of optical switches.

[0034] The second layer of optical switches 70, 72, 74, and 76 comprise a second layer of single pole quadruple throw optical switches. There are four of these 4×1 switches in this second layer. Each switch selects one of four waveforms from the oven that is currently heating up and distributes it to four channels of the oscilloscope. One acquisition of the oscilloscope allows four waveforms to be acquired simultaneously. In this description, these switches are referred to as "channel" switches because each of these channel switches connects to one channel of the test and measurement instrument 52.

[0035] In some embodiments, an optical splitter may be used as in the embodiment of FIG. 1. Splitter 40 picks up a portion of the signal from one of the four optical switch output signals and applies it to the CLK recovery input of the sampling oscilloscope. This allows the oscilloscope to recover the clock and acquire the signal for each channel. In the embodiment of FIG. 5, the oscilloscope may be an equivalent time (ET) oscilloscope, which is an example of a sampling oscilloscope, as the test and measurement instrument. An embodiment using an ET oscilloscope would use splitter 40. An embodiment using a real-time (RT) oscilloscope would not have splitter 40 and would not have a connection to the CLK of test and measurement instrument 52.

[0036] Further processing may be required in embodiments where an optical splitter is required to split off a portion of the signal for input to the clock recovery input of the oscilloscope. The sampling oscilloscope may have mathematical scale factors that need to be applied to its input waveform to make it compatible with the waveforms on the other three channels of the oscilloscope. Scale blocks and splitters are not required in embodiments using a RT oscilloscope.

[0037] In the embodiment of Figure 5, the test and measurement instrument acquires waveforms from the transmitter. One important aspect of this system is that four channels are acquired at a time in parallel. The time it takes to tune the transmitter depends only on the oscilloscope acquisition time and the DSP processing time of the optical tuning application.

[0038] Similar to Figure 1, the parallel channel acquisition embodiment of Figure 4 has the customer test automation application 12 as the main controller of the entire system. It is responsible for the timing and sequencing of tasks in all four parallel pipelines. This includes setting the oven temperature, pausing while an operator or robot loads and unloads transmitters from the oven, handling the loading of tuning parameters into the transmitters, and controlling the test and measurement equipment to acquire waveforms from the transmitters.

[0039] This also includes collecting and sending waveforms and parameters to the optical tuning application to train the deep learning network and receiving in return predictions of optimal tuning parameters and waveform measurements for tuning validation. This control also includes controlling the first layer of 2×1 optical switches to select the transmitter from which to collect the four waveforms in the ovens that are currently increasing in temperature. The customer application 12 controls the second layer of switches to select the pipeline of ovens that are increasing in temperature and also controls the oscilloscope to get the waveforms from the oscilloscope while keeping track of which ovens are currently increasing in temperature for tuning actions. The timing includes 96 seconds of unused time intervals that can be additionally allocated for unspecified actions.

[0040] Block 14 is a machine learning system 14, similar to FIG. 1. This block uses a deep learning network to predict optimal tuning parameters and speed up measurements. The customer application and the machine learning system can reside on a computing device (host PC) 16, which may have a graphics processing unit (GPU). According to some embodiments, to obtain the desired speedup times, the host PC may have a clock speed of 5 GHz and use a GPU.

[0041] In FIG. 4, the pipeline task structure specifies how tasks are performed, some in parallel and some in serial. This shows four parallel pipelines served by four ovens. The timing block shows the 96 second time interval for loading and unloading the transmitters from the ovens. Due to the nature of the parallel pipeline structure, this timing block is effectively 0 seconds. The larger blocks represent the oven ramp times that require temperature changes. These are 180 seconds in length. These are also effectively zero seconds, and tuning times depend only on the length of time of the CHX 60s box. The blocks labeled CHX (X is 1, 2, 3, or 4 in this embodiment) represent the time to tune the eight transmitters at a given oven temperature. The time for each transmitter is 2-3 seconds for one parallel acquisition of four waveforms, 1 second for the DSP to predict tuning parameters, 2 seconds for the traditional TDECQ calculation, and 1 second margin. These greenBlocks are the calculation and acquisition times, see the Mathcad® model formula below. Each of the CHX 60s blocks performs calculations while the other three ovens are at different stages of temperature ramping. After each CHX 60s block there is another temperature block for that pipeline to ramp up to the next test temperature. After each pipeline calculates the adjustments for each temperature, it is ready to unload the 8 transmitters in that oven and load a new set of 8 transmitters to repeat the cycle. The other blocks are spare unused time intervals, allowing the system to perform other operations that may not be covered by the light adjustment algorithm. These grey blocks may be split to include after each CHX 60s block to perform other operations at each temperature, adjusting the pipeline sequence time as needed. These blocks require effectively 0 seconds to execute.

[0042] The equation in the Mathcad® model is as follows: [Number 1] refPar := 3 scopeAcq:= 2 seconds TDECQ := 2 seconds Tune:= 1 second numTemp:=3 numTXperOven:= 8 nominalTune:= 15 60 seconds numChannels: = 4 The estimated time for the parallel oven pipeline using parallel channels of the oscilloscope is: 1 Regarding the DUT / Transmitter:

number

[0043] The end result of the parallel pipeline, as shown in Figure 4, is that it outputs fully tuned transmitters at an average ideal rate of one transmitter every 22.5 seconds, for tuning at three temperatures. This given tuning time is ideal assuming that the machine learning predictions are 100% accurate. However, there is also an overlap margin of 96 seconds to cover any inaccuracies to some extent or completely. However, the actual rate of tuned transmitters may be somewhat slower, since failed transmitters will need to be tested by the user, slowing down the tuning algorithm. Nevertheless, the actual results are expected to be many times faster than the current methods used on the manufacturing line.

[0044] By way of example, and for ease of explanation and understanding, reference is made to Figures 4 and 5. As an example of a switching sequence, for the pipeline of oven 20 in Figure 5, it is as follows: 1. Load 8 transmitters into the first oven (Oven 1) 20. 2. Begin heating oven 20 to the first temperature and wait 180 seconds. While oven 20 is heating, load eight transmitters into second oven (oven 2) 22 and begin heating oven 22 to the first temperature for 180 seconds. While oven 22 is heating, load eight transmitters into third oven (oven 3) 24 and begin heating oven 24 to the first temperature for 180 seconds. While oven 24 is heating, load eight transmitters into fourth oven 26 and begin heating oven 26 to the first temperature for 180 seconds. 3. Using switches 60, set the first tier of switches to select the four Tx's in oven 20. 4. Set the second layer of switches to apply the four transmitters of the first oven to the four channels of the oscilloscope. In Figure 5, switches 70, 72, 74 and 76 receive waveforms from transmitters 1, 3, 5 and 7, respectively, from oven 20. 5. Set the first set of reference tuning parameters to Tx. 6. Acquire four Tx waveforms from each of the four transmitters originally selected. 7. Set switches 60 in the first tier of switches to select the second 4 Tx in oven 20. 8. Acquire four Tx waveforms from each of the second set of four selected transmitters in the first oven. Switches 70, 72, 74 and 76 each receive waveforms from transmitters 2, 4, 6 and 8, respectively, from oven 20. 9. Set the second set of reference parameters to Tx and acquire the second four waveforms. 10. Change the first layer switches to select another four transmitters, where this other subset consists of transmitters 1, 3, 5 and 7. 11. Acquire four more waveforms in one acquisition cycle in parallel on four channels of the oscilloscope. 12. Set the third set of reference tuning parameters in the transmitter. 13. Acquire four Tx waveforms in one oscilloscope acquisition. 14. Change the first tier switches to select the other 4 Tx in the first oven. This switches the transmitter to the second subset 2, 4, 6 and 8. 15. Acquire four waveforms in a single oscilloscope acquisition. 16. Sequence the three waveforms from each transmitter at temperature 1 to the OptaML tuning prediction block and receive a set of predicted optimal tuning parameters for each of the 8 Tx in Oven 1. 17. The eight optimal tuning parameter sets are set on the associated transmitters. 18. Acquire four waveforms from the first four transmitters. 19. Change the settings of the first tier switches to select the second four transmitters. 20. Acquire four waveforms in a single oscilloscope acquisition. 21. Calculate TDECQ and other measurements for each of the eight acquired waveforms to determine if each transmitter is optimally tuned. 22. Change oven 1 and start ramping up to temperature 2. 23. At this point, the second oven has reached its 180 second soak time for Temperature 1. 24. Flip the switch on the second layer to select the transmitter for the second oven. 25. Repeat steps 3 to 23, but use the second oven at temperature 1. 26. Flip the switch on the second layer to select the transmitter for the third oven. 27. Repeat steps 3 to 23, but use oven number 3 at temperature 1. 28. Flip the switch on the second layer to select the transmitter for the fourth oven. 29. Repeat steps 3 to 23, but use oven number 4 at temperature 1. 30. Flip the switch on the second layer to select the transmitter for the first oven. 31. Repeat steps 3 to 23, but use oven 1 at temperature 2. 32. Flip the switch on the second layer to select the transmitter for the second oven. 33. Repeat steps 3 to 23, but use the second oven at temperature 2. 34. Flip the switch on the second layer to select the transmitter for the third oven. 35. Repeat steps 3 to 23, but use oven number 3 at temperature 2. 36. Flip the switch on the second layer to select the transmitter for the fourth oven. 37. Repeat steps 3 to 23, but use oven number 4 at temperature 2. 38. Switch the second layer switch to select the transmitter for the first oven. 39. Repeat steps 3 to 23, but use oven 1 at temperature 3. 40. Unload the eight tuned transmitters from the first oven and load and tune the eight new transmitters. 41. Repeat steps 3 to 23, but use the second oven at temperature 3. 42.Output eight regulated transmitters from the second oven at all three temperatures. 43. Unload the 8 transmitters from the 3rd oven and load and tune the 8 new transmitters. 44.Output eight regulated transmitters from the third oven at all three temperatures. 45. Repeat steps 3 to 23, but use oven number 4 at temperature 3. 46. ​​Power up the 8 tuned transmitters in oven #4 and load and tune the 8 new transmitters. 47. Keep repeating the above cycle to continuously output the tuned transmitter with essentially zero time to load, unload and temperature rise. The time depends only on the oscilloscope acquisition time and the DSP time to estimate the tuning parameters and verify with TDECQ.

[0045] Figures 6 and 7 show close-up views of the first and second layers of the optical switch. Figure 6 shows an embodiment of half of switch 60, but it is applicable to switches 62, 64 and 66. These types of switches are commercially available, such as part number OS-3221 from LuxLink, USA. In a first state, this half of the switch selects DUTs 1 and 3 from the first oven, i.e. 1-1 and 1-3. In a second state, the switch selects DUTs 1-2 and 1-4. The other half of the switch has the same configuration, but selects either 1-5 and 1-7 or 1-6 and 1-8.

[0046] Figure 7 shows a close-up view of single-throw quad-pole switches such as 70, 72, 74 and 76 in Figure 5. These types of switches are commercially available from LuxLink, USA, such as part number OS-3141. The current configuration is for switch 70 to connect to channel 1 of the test and measurement instrument. The first setting is for the first oven. Returning to the sequence described above, after acquiring all three sets of waveforms from all eight DUTs, for two subsets of four DUTs the switch will acquire waveforms from the second oven, etc.

[0047] While the above description focuses on a number of DUTs per oven, a number of ovens, and two layers of switches, it is not intended and should not be assumed that the invention is limited to these particular features. In general, embodiments of the disclosed technology may include a generalized optical switch matrix that can connect any number of ovens, with any number of transmitters in each oven, to four channels of an oscilloscope for parallel acquisition, as shown in FIG. 9. This basic test configuration, combined with a new block of machine learning assisted tuning prediction of optical transmitters, constitutes this new invention.

[0048] As an example of other features of the test system, Figure 8 shows a parallel oven pipeline using only two ovens. This requires a larger number of transmitters per oven, 32, and the calibration process times shown in the CHX 60s blocks take longer than the time it takes for the ovens to come up to temperature. These are the longer blocks. The "swap" block represents the time it takes to unload a transmitter from the oven and load a transmitter into the oven for the next test round. As long as the CHX 60s block's prediction time is longer than the temperature block's temperature rise time plus the swap block's load / unload time, the total calibration time depends only on the oscilloscope acquisition time plus the DSP prediction time plus the overhead associated with this. This means that the temperature rise time and oven load and unload times are effectively zero and do not affect the overall speed of calibration.

[0049] FIG. 9 shows a more generalized block diagram, illustrating that any number of ovens 80, 82, and 84 can hold a variety of transmitters. As long as there are enough transmitters in each oven so that the heat-up time is less than the time it takes for the oven to process the transmitters, this embodiment can run at full speed, relying only on the predictive channel (CH) processing block to determine the speed of adjustment. The layer of switches can be altered as needed to form an optical switch matrix 90. As mentioned above, this embodiment illustrates an equivalent-time (ET) or other sampling device that requires clock recovery using splitter 40. With other test and measurement devices, such as real-time (RT) oscilloscopes, this is not necessary.

[0050] These embodiments use an additional layer of optical switches, which enable parallel oscilloscope acquisition and are used in the pipeline. The machine learning assisted optical tuning block is combined with a novel parallel oven pipeline, oscilloscope parallel channel acquisition and switching architecture to effectively eliminate multiple oven cycle times and to speed up the prediction of optimal tuning parameters with the aid of machine learning. This combination of features ideally provides a 320x improvement in tuning speed over a customer's worst case where tuning takes 2 hours.

[0051] Aspects of the disclosed technology can operate on specially created hardware, firmware, digital signal processors, or specially programmed general-purpose computers, including processors that operate according to programmed instructions. The term "controller" or "processor" in this application contemplates microprocessors, microcomputers, ASICs, and dedicated hardware controllers, among others. Aspects of the disclosed technology can be implemented in computer-available data and computer-executable instructions, such as one or more program modules, executed by one or more computers (including a monitoring module) or other devices. Generally, program modules include routines, programs, objects, components, data structures, and the like, which, when executed by a processor in a computer or other device, perform particular tasks or implement particular abstract data formats. The computer-executable instructions may be stored in computer-readable storage media, such as hard disks, optical disks, removable storage media, solid-state memory, RAM, and the like. As will be appreciated by those skilled in the art, the functionality of the program modules may be combined or distributed as desired in various embodiments. Furthermore, such functionality may be embodied in whole or in part in firmware or hardware equivalents, such as integrated circuits, field programmable gate arrays (FPGAs), etc. Certain data structures may be used to more effectively implement one or more aspects of the disclosed techniques, and such data structures are considered to be within the scope of the computer-executable instructions and computer-usable data described herein.

[0052] The disclosed aspects may, in some cases, be implemented in hardware, firmware, software, or any combination thereof. The disclosed aspects may also be implemented as instructions carried by or stored on one or more computer-readable media that may be read and executed by one or more processors. Such instructions may be referred to as computer program products. A computer-readable medium as described herein means any medium that can be accessed by a computing device. By way of example, and not limitation, computer-readable media may include computer storage media and communication media.

[0053] Computer storage media means any medium that can be used to store computer-readable information. By way of example and not limitation, computer storage media may include random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital video disc (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, and any other volatile or non-volatile removable or non-removable media implemented in any technology. Computer storage media excludes signals themselves and transitory forms of signal transmission.

[0054] A communication medium refers to any medium capable of communicating computer readable information. By way of example, and not limitation, communication media may include coaxial cables, fiber optic cables, air, or any other medium suitable for communicating electrical, optical, radio frequency (RF), infrared, acoustic, or other types of signals. Working Example

[0055] In the following, examples useful for understanding the technology disclosed in the present application are presented. The embodiment of the technology may include one or more of the examples described below and any combination thereof.

[0056] Example 1 is a test system, a test and measurement instrument having a plurality of channels; an oven configured to hold a plurality of devices under test (DUTs); oven switches each selectively connected to the DUT in a corresponding one of the ovens; channel switches selectively connected to the oven switches, each of the channel switches being connected to one of the channels of the test and measurement instrument; One or more processors Equipped with the one or more processors selecting the oven as a selected oven and a corresponding oven switch as a selected oven switch; controlling said selection oven switches to connect to a subset of DUTs within said selection oven; connecting the channel switch to the selected oven switch to receive signals from the subset of DUTs in the selected oven and transmit the signals to the channels of the test and measurement instrument to acquire waveforms from the subset of DUTs in parallel; repeating control of the channel switch and the selection oven switch until acquiring waveforms from each of the DUTs in the selection oven; tuning each of the DUTs in the selected oven to a set of parameters using machine learning; acquiring waveforms from DUTs in the selected ovens and calculating measurements of the waveforms to determine whether each of the DUTs in the selected ovens is optimally tuned; repeating the process of selecting a next oven and a corresponding next oven switch and controlling the channel switch until each of the DUTs in each of the ovens is conditioned and tested. The one or more processors are configured to execute a program that causes the one or more processors to perform the steps of:

[0057] A second embodiment is the test system of the first embodiment, wherein the number of the channel switches corresponds to the number of the channels of the test and measurement instrument.

[0058] Example 3 is the test system of any one of Examples 1 and 2, wherein the oven switch has a number of switches based on the number of DUTs in each of the ovens.

[0059] Example 4 is a test system of any of Examples 1 to 3, wherein the one or more processors are further configured to control the oven to cycle the oven through a plurality of temperatures, and the one or more processors repeat control of the selection oven switch and the channel switch for each of the temperatures.

[0060] A fifth embodiment is the test system according to any one of the first to third embodiments, in which the DUT is one of an electronic device, an optical transceiver, and an optical transmitter.

[0061] Example 6 is a test system of any of Examples 1 to 3, wherein the program for causing the one or more processors to perform a process of adjusting each of the DUTs using machine learning includes a program for causing the one or more processors to perform a process of performing a sequence of sending waveforms acquired from each of the DUTs in the selected oven to a machine learning network, and a process of analyzing the waveforms using the machine learning network and providing adjustment parameters for each of the DUTs.

[0062] Example 7 is the test system of Example 6, wherein the one or more processors are further configured to control an oven to cycle through a plurality of temperatures, and the one or more processors repeat a sequence of processes of transmitting acquired waveforms and using a machine learning network for each temperature of each of the DUTs in each of the ovens.

[0063] Example 8 is a test system of any of Examples 1 to 3, comprising four ovens, each holding eight optical transmitters, four oven switches, each configured to select a subset of the four optical transmitters, and four channel switches, each connecting between the four oven switches and one of the channels of the test and measurement instrument.

[0064] A ninth embodiment is the test system of any one of the first to third embodiments, in which the test and measurement instrument comprises an oscilloscope.

[0065] Example 10 is a method for testing devices under test (DUTs) in a plurality of ovens, comprising: selecting one oven from the plurality of ovens as a selected oven and selecting a corresponding oven switch as a selected oven switch; controlling said selection oven switches to connect to a subset of DUTs within said selection oven; connecting a channel switch to the selected oven switch to receive signals from the subset of DUTs in the selected oven and transmit the signals to channels of a test and measurement instrument to control acquisition of waveforms from the subset of DUTs in parallel; repeating control of the channel switch and the selection oven switch until acquiring a waveform from each of the DUTs in the selection oven; tuning each of the DUTs in the selected oven to a set of parameters using machine learning; acquiring waveforms from DUTs in the selected ovens and calculating measurements of the waveforms to determine whether each of the DUTs in the selected ovens is optimally tuned; repeating the process of selecting a next oven and a corresponding next oven switch and controlling the channel switch until each of the DUTs in each of the ovens is conditioned and tested. Equipped with.

[0066] Example 11 is the method of example 10, wherein a number of the channel switches corresponds to a number of channels on the test and measurement instrument.

[0067] Example 12 is the method of any of examples 10 or 11, wherein the oven switches include a number of switches based on a number of the DUTs in each of the ovens.

[0068] Example 13 is a method of any of Examples 10 to 12, further comprising controlling the oven to cycle the oven through a plurality of temperatures, and repeating the control of the selection oven switch and the channel switch for each of the temperatures.

[0069] Example 14 is the method of any one of Examples 10 to 13, in which the DUT is one of an electronic device, an optical transceiver, or an optical transmitter.

[0070] Example 15 is a method of any of Examples 10 to 14, wherein the process of adjusting each of the DUTs using machine learning includes a process of performing a sequence of sending waveforms acquired from each of the DUTs in the selected oven to a machine learning network, and a process of analyzing the waveforms using the machine learning network to provide adjustment parameters for each of the DUTs.

[0071] Example 16 is the method of example 15, further comprising repeating the steps of controlling the oven to cycle through a plurality of temperatures, performing a sequence of transmitting acquired waveforms, and using a machine learning network for each of the temperatures of each of the DUTs in each of the ovens.

[0072] Example 17 is any of the methods of Examples 10 to 16, wherein the process of using the machine learning system includes a process of receiving a waveform from each of the DUTs, and a process of applying machine learning to the waveforms to generate adjustment parameters for the DUT.

[0073] Example 18 is the method of any of examples 10-17, wherein the test and measurement instrument comprises an oscilloscope.

[0074] Example 19 is a test system comprising: a test and measurement instrument having a plurality of channels; an oven configured to hold a plurality of devices under test (DUTs), each configured to hold a number of DUTs such that a ramp-up time is shorter than the time to process all of the DUTs in one oven; a switch matrix configured to connect between the DUTs in each of the ovens and channels of the test and measurement instrument; One or more processors Equipped with said one or more processors: controlling the switch matrix to connect to at least a portion of the DUTs in an initial set of ovens set at a first temperature and acquire a set of waveforms using the test and measurement instrument; setting the next set of ovens to their initial temperature; sending said set of waveforms to a machine learning network; receiving optimal tuning parameters for the DUT at the initial temperature; testing the DUT using the optimal tuning parameters to determine whether the DUT is optimally tuned; setting the initial set temperature of the oven to the following temperature; repeating the controlling, transmitting, receiving and testing processes for the next set of ovens at a next temperature until all DUTs in all ovens have been tested at all temperatures in the predefined test temperature set. The one or more processors are configured to execute a program that causes the one or more processors to perform the steps of:

[0075] Example 20 is the test system of example 19, in which the test and measurement instrument comprises an oscilloscope.

[0076] In addition, the description of this application refers to specific features. It should be understood that the disclosure herein includes all possible combinations of those specific features. When a specific feature is disclosed in connection with a particular embodiment or example, that feature can also be used in connection with other embodiments, to the extent possible.

[0077] Furthermore, when the application refers to a method having two or more defined steps or processes, those defined steps or processes may be performed in any order or simultaneously, unless the context precludes such a possibility.

[0078] All features disclosed in the specification, claims, abstract and drawings, and all steps in any disclosed method or process, may be combined in any combination, except where at least some of such features or steps are mutually exclusive combinations. Each feature disclosed in the specification, abstract, claims and drawings may be replaced by an alternative feature serving the same, equivalent or similar purpose, unless expressly stated otherwise.

[0079] For convenience of illustration, specific embodiments of the invention have been illustrated and described, but it will be understood that various modifications can be made therein without departing from the spirit and scope of the invention. Accordingly, the invention should not be limited, except as by the appended claims. [Explanation of symbols]

[0080] 10 Pipeline-based test system 12 Customer Test Automation Applications 14 Machine Learning Systems 16 Computing Device (Host PC) 20 Oven 22 Oven 24 Oven 26 Oven 30 Oven DUT Switch 32 Oven DUT Switch 34 Oven DUT Switch 36 Oven DUT Switch 40 Splitter 42 Splitter 44 Splitter 46 Splitter 50 Test and Measurement Equipment Switch 52 Test and measurement equipment 60 Light oven switch 62 Light oven switch 64 Light Oven Switch 66 Light oven switch 70 Optical Channel Switch 72 Optical Channel Switch 74 Optical Channel Switch 76 Optical Channel Switch 80 Oven 82 Oven 84 Oven 90 Optical Switch Matrix

Claims

1. a test and measurement instrument having a plurality of channels; an oven configured to hold a plurality of devices under test (DUTs); oven switches each selectively connected to said DUT within a corresponding one of said ovens; channel switches selectively connected to the oven switch, each of the channel switches being connected to one of the channels of the test and measurement instrument; One or more processors Equipped with the one or more processors selecting the oven as a selected oven and a corresponding oven switch as a selected oven switch; controlling the selected oven switches to connect to a subset of DUTs within the selected oven; connecting said channel switch to said selected oven switch and controlling said selected oven switch to receive signals from said subset of DUTs and transmit said signals to said channels of said test and measurement instrument to acquire waveforms from said subset of DUTs in parallel; repeating the control of the channel switch and the selected oven switch until acquiring a waveform from each of the DUTs in the selected oven; tuning each of the DUTs in the selected oven to a set of parameters using machine learning; acquiring waveforms from DUTs in said selected ovens and calculating measurements of said waveforms to determine whether each of said DUTs in said selected ovens is optimally tuned; repeating the process of selecting a next oven and a corresponding next oven switch and controlling said channel switch until each of said DUTs in each of said ovens has been conditioned and tested; a test system configured to execute a program that causes the one or more processors to perform the steps of:

2. 2. The test system of claim 1, wherein the one or more processors are further configured to control the oven to cycle the oven through a plurality of temperatures, the one or more processors repeating control of the selection oven switch and the channel switch for each of the temperatures.

3. A program for causing the one or more processors to perform a process of adjusting each of the DUTs using machine learning, performing a sequence of transmitting acquired waveforms from each of the DUTs in the selected oven to a machine learning network; analyzing the waveform using the machine learning network and providing tuning parameters for each of the DUTs; 2. The test system according to claim 1, further comprising a program for causing said one or more processors to perform the steps of:

4. 4. The test system of claim 3, wherein the one or more processors are further configured to control an oven to cycle through a plurality of temperatures, the one or more processors repeating a sequence of transmitting acquired waveforms and using a machine learning network for each temperature of each of the DUTs in each of the ovens.

5. There are four ovens, each holding eight optical transmitters, four oven switches, each configured to select a subset of the four optical transmitters; There are four channel switches each connecting between the four oven switches and one of the channels of the test and measurement instrument. The test system of claim 1 .

6. 1. A method for testing devices under test (DUTs) in a plurality of ovens, comprising: selecting one oven from the plurality of ovens as a selected oven and selecting a corresponding oven switch as a selected oven switch; controlling the selected oven switches to connect to a subset of DUTs within the selected oven; controlling a channel switch to connect to the selected oven switch to receive signals from the subset of DUTs in the selected oven and transmit the signals to channels of a test and measurement instrument to acquire waveforms from the subset of DUTs in parallel; repeating the control of the channel switch and the selected oven switch until acquiring a waveform from each of the DUTs in the selected oven; tuning each of the DUTs in the selected oven to a set of parameters using machine learning; acquiring waveforms from DUTs in said selected ovens and calculating measurements of said waveforms to determine whether each of said DUTs in said selected ovens is optimally tuned; repeating the process of selecting a next oven and a corresponding next oven switch and controlling said channel switch until each of said DUTs in each of said ovens has been conditioned and tested; A method for testing a device under test comprising:

7. 7. The method of testing a device under test of claim 6, further comprising the steps of: controlling the oven to cycle the oven through a plurality of temperatures; and repeating the control of the selected oven switch and the channel switch for each of the temperatures.

8. The process of tuning each of the DUTs using machine learning includes: performing a sequence of transmitting acquired waveforms from each of the DUTs in the selected oven to a machine learning network; analyzing the waveform using the machine learning network and providing tuning parameters for each of the DUTs; 7. The method for testing a device under test according to claim 6, comprising:

9. The process of using machine learning systems is receiving a waveform from each of the DUTs; applying machine learning to the waveform to generate tuning parameters for the DUT; 7. The method for testing a device under test according to claim 6, comprising:

10. a test and measurement instrument having a plurality of channels; an oven configured to hold a plurality of devices under test (DUTs), each oven configured to hold a large enough number of DUTs that the ramp-up time is shorter than the time to process all the DUTs in one oven; a switch matrix configured to connect between the DUTs in each of the ovens and channels of the test and measurement instrument; One or more processors Equipped with the one or more processors controlling the switch matrix to connect to at least a portion of the DUTs in an initial set of ovens set at a first temperature and acquire a set of waveforms using the test and measurement instrument; setting the next set of ovens to their initial temperature; sending said set of waveforms to a machine learning network; receiving optimal tuning parameters for the DUT at said initial temperature; testing the DUT using the optimal tuning parameters to determine whether the DUT is optimally tuned; setting the initial set temperature of the oven to the following temperature; repeating the controlling, transmitting, receiving and testing processes at a next temperature for the next set of ovens until all DUTs in all ovens have been tested at all temperatures in the predefined test temperature set. a test system configured to execute a program that causes the one or more processors to perform the above steps.

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