Abstract:
The Unique Neural Network Signal Processor/Analyzer (UNSPA), for real-time analysis of analog signals, involves combining acoustic charge transport (ACT) device(s) and an artificial neural network processor on a single, monolithic substrate. The ACT will act as input to the neural network. The UNSPA will allow non-destructive, high-speed, real-time signal analysis with improved performance and decreased size over conventional methods. The UNSPA can function as, but is not limited to, signal classification, prediction or error detection. Applications for such functions could be: target or speech recognition, signal prediction, sensor fusion, adaptive control, image classification, in-line error detection.

Description:
RIGHTS OF THE GOVERNMENT 
     The invention described herein may be manufactured and used by or for the Government of the United States for all governmental purposes without the payment of any royalty. 
    
    
     BACKGROUND OF THE INVENTION 
     The present invention relates generally to a Unique Neural Network Signal Processor/Analyzer (UNSPA), and more particularly to a Processor/Analyzer which will allow real-time analysis of analog signals. 
     Currently, real-time analysis can be accomplished via analog-to-digital converters with digital circuity, then reconstructed by digital-to-analog converters. This method results in quantization errors, degradation of signal and long processing delays. Even if artificial neural networks (henceforth neural nets) are used for analysis, the signal must first be put though an A/D converter (digitized), and then the digital word is analyzed via a microprocessor neural net system (usually a software program in a computer). This method also suffers quantization errors, and requires a large computer system for the neural net. 
     The following U.S. patents relating to neural networks are of interest. 
     U.S. Pat. No. 5,109,351--Simar 
     U.S. Pat. No. 5,067,095--Peterson et al 
     U.S. Pat. No. 4,918,617--Hammerstrom et al 
     U.S. Pat. No. 4,884,216--Kuperstein 
     The patent to Peterson discloses the known usage of artificial neural networks but lacks any discussion of acoustic charge transport. The patent to Hammerstrom et al discloses neural-model computation system with multi-directionally overlapping broadcast. The patent to Kuperstein discloses a neural system for adaptive sensor-motor coordination of multijoint robots for single postures. The patent to Simar discloses backward pass learning methods for feedforward neural type devices and network architectures for these methods. 
     The following United States patents relating to Acoustic Charge Transport (ACT) are of interest: 
     U.S. Pat. No. 4,752,750--Zimmerman et al 
     U.S. Pat. No. 4,884,001--Sacks et al 
     U.S. Pat. No. 5,063,390--Konig 
     The patent to Sacks et al discloses a novel heterojunction acoustic charge transport device which includes a modulation doped field effect transistor (MODFET) on the same substrate. The device may be comprised of GaAs and AlGaAs which is both piezoelectric and semiconducting. The patents to Zimmerman et al and Konig show ACT devices with taps. 
     SUMMARY OF THE INVENTION 
     An objective of the invention is to provide a Processor/Analyzer which will allow non-destructive, high-speed, real-time signal analysis with improved performance and decreased size over conventional methods. 
     The invention relates essentially to a Unique Neural Network Signal Processor/Analyzer (UNSPA) which will allow real-time analysis of analog signals. This invention involves combining acoustic charge transport (ACT) device(s) and an artificial neural network processor on a single, monolithic substrate. The ACT will act as input to the neural network. 
     This invention can function as, but is not limited to, signal classification, prediction or error detection. Applications for such functions could be: target or speech recognition, signal prediction, sensor fusion, adaptive control, image classification, and in-line error detection. 
    
    
     BRIEF DESCRIPTION OF THE DRAWING 
     FIG. 1 is a diagram showing an acoustic charge transport device (ACT) and an artificial neural network processor on a single, monolithic substrate; 
     FIG. 2 is a diagram showing a processor/analyzer implemented with a generic neural network; and 
     FIG. 3 is a diagram showing an error designation structure for a processor/analyzer utilizing an Adaptive Resonance Theory (ART) network. 
    
    
     DETAILED DESCRIPTION 
     Acoustic charge transport (ACT) devices enable analog signals to be non-destructively sensed via taps along the surface of the device. Neural nets allow data to be studied in a non-traditional method, yielding unique results for pattern recognition, prediction, classification and adaptive processing. Combining ACT with neural nets in a monolithic gallium-arsenide (GaAs) implementation will result in a powerful, new real-time signal analysis device. 
     Referring to FIG. 1, there is shown a combination of acoustic charge transport with artificial neural networks, which is believed to be a novel idea with many intrinsic advantages. Since the UNSPA is to be built on a monolithic GaAs chip 10 it will be small and compact. ACT requires less power than ADCs and have a transfer efficiency better than 0.9999 with no quantization error. The ACT is composed of an input on line 12, a channel 14 and a surface acoustic wave driver 16. The taps 18 will sense the charge of the signal in the channel, which can be attenuated in attenuators 20 and a summed output 22 created. This summed output (Σ) can be used directly as a filtered signal or input to the neural net 24 for error calculation (or other functions). Each tap will be an input to a neural network 24 whose output on line 26 can be fed back at line 28 to the neural net for training, etc. The original signal can exit the ACT at line 30 virtually unchanged. 
     The processor/analyzer will be versatile in its design: the tap quantity and spacing is selectable; the type of neural net is selectable (n-layer back-propagation, feedback/feedforward and lateral inhibition, Kohonen, time delay neural net, etc. . . .). The speed of the UNSPA will allow real-time analysis of high frequency signals which previously could not be analyzed in real-time. 
     ALTERNATIVES: FIG. 2 shows the processor/analyser implemented with a generic neural network. Many types of neural nets may be substituted for the genetic form. The function of the processor/ analyzer will be determined by the type of neural net used. These functions may comprise, but are not limited to: 
     Signal Classification 
     Signal Prediction 
     Error Detection 
     Signal/Data Fusion 
     Adaptive Control 
     Multiple devices may be combined to provide for n-dimensional analysis. The neural nets may be trained on-chip or trained off-chip with the weights loaded onto the chip. A total hardware implementation will gain in the area of speed and compactness, while a hardware/software implementation will provide greater flexibility in design. 
     Basic UNSPA Operation 
     1) A signal at line 12 is sampled by a surface acoustic wave (SAW) device at its input contact (source). 
     2) Charge packets are transported in the ACT/HACT channel 14. 
     3) Taps over the transport channel non-destructively sense the charge in the packets passing beneath. 
     4) The output voltage on each tap may vary from 50 microvolts-100 millivolts, depending upon the input voltage. 
     5) By attenuating the tap outputs with the attenuators 20, the signal can be normalized before input to the artificial neural network 24. 
     6) The normalized data enters the &#34;neurons&#34; and is multiplied by a weighting variable (see note below), then all inputs to each neuron are summed. This sum is passed through a thresholding function and this value is passed to the next level of &#34;neurons&#34;. 
     7) Depending on the function of the system, the final output may be compared to a &#34;desired&#34; output (during training). The difference between these values are used to adjust the weights of the neurons. This continues until the difference is minimized to a specified level. 
     8) Once the network is &#34;trained&#34;, normal operation of the UNSPA can commence. 
     9) The charge packets continue in the ACT channel until they are extracted at the output (drain). The efficiency of the transport channel exceeds 0.99995. 
     NOTE: The example in 6) and 7) above was for a generic, multi-level, back propagation network system. Other neural paradigms exist and will differ in structure as well as function. Other types of neural networks that can be used in UNSPA are: Kohonen self-organizing networks, counter propagation (feedforward), localized receptive field networks, adaptive resonance theory, and time-delay neural networks. All these networks can be connected to the ACT device taps in a similar way. The choice of a particular network paradigm will be dependent on what function is desired from UNSPA as well at the input signal characteristics. 
     Error Detection Function 
     Depending on the system, errors will tend to be aberrations and will therefore occur as low probability events. The UNSPA can be used to detect when these aberrations occur during normal system operation. This can be accomplished &#34;in-line&#34; without degradation to system signals. A threshold hold level can be adjusted to control the degree of error detection (i.e. to minimize minor aberrations being called &#34;errors&#34;). Fuzzy logic can also be implemented to provide a &#34;certainty factor&#34; when aberrations are encountered. 
     One method of training such a system is implementing an unsupervised learning algorithm such as a Kohonen networks, Adaptive Resonance Theory (ART) networks, or self-organizing localized receptive fields. These algorithms are useful when data is acquired at high rate and cannot be saved and fast processing is required (real-time applications). These &#34;self-organizing&#34; paradigms are excellent for sorting data into classes. By using input data parameters, the network clusters &#34;like&#34; data in classes on the hyperplane. For example, in this application, these classes could be: 
     Normal Operation, Error type 1, Error type 2, . . . etc. A percentage of certainty could be associated, such as: ##EQU1## For example, the structure of UNSPA utilizing an ART2 network is shown in FIG. 3. 
     The analog signals from the taps off the ACT enter the Comparison Layer. There are weights between all layers: to and from (not all are shown). Via feedback and weight adjustment between the neurons of the Comparison and Recognition levels, inputs are either clustered with prior exemplars or become a new cluster exemplar. The vigilance is the distance scalar which determines classification: if the &#34;distance&#34; between an input and its closest examplar are less that the vigilance, then it is clustered with that class; otherwise, a new cluster is formed for that input. 
     A VLSI implementation of the neural portion of UNSPA would be in the range of 10K to 100K gates, depending on the number of taps of the ACT channel and the complexity of the total system. A software/hardware implementation of this system would be simpler, but slower. Neural network chips exist which could be adapted to connect to the ACT taps and would be software controllable. 
     It is understood that certain modifications to the invention as described may be made, as might occur to one with skill in the field of the invention, within the scope of the appended claims. Therefore, all embodiments contemplated hereunder which achieve the objects of the present invention have not been shown in complete detail. Other embodiments may be developed without departing from the scope of the appended claims.