A. Moopenn

Solid‐state thin‐film memistor for electronic neural networks

We report on a tungsten‐oxide‐based, nonvolatile, electrically reprogrammable, variable resistance device as an analog synaptic memory connection for electronic neural networks. A voltage controlled, reversible injection of H+ ions in electrochromic thin films of WO3 is utilized to modulate its resistance. A hygroscopic thin film of Cr2 O3 is the source of H+ ions. The resistance of the device can be tailored and stabilized over a wide dynamic range (∼four orders of magnitude), and the programming speed is modulated by the control voltage. The suitability of such a device in terms of its response speed, reversibility, stability, and cyclability for its use in electronic neural networks is discussed.

Electronic hardware implementations of neural networks

This paper examines some of the present work on the development of electronic neural network hardware. In particular, the investigations currently under way at JPL on neural network hardware implementations based on custom very large scale integrated technology, novel thin film materials, and an analog-digital hybrid architecture are reviewed. The availability of such hardware will greatly benefit and enhance the present intense research effort on the potential computational capabilities of highly parallel systems based on neural network models.

Binary synaptic connections based on memory switching in a‐Si:H

Nonvolatile, associative, electronic memory based on neural network models promises high (∼109 bits/cm2) information storage density since the information would be stored in a matrix of simple two terminal, passive interconnections. Device considerations dictate that such connections should be weak. For example, connections with ∼106Ω resistance are quite adequate in a 1000×1000 matrix (∼250K bit ROM). Irreversible memory switching in hydrogenated amorphous silicon (a‐Si:H) thin films is studied as a candidate mechanism for a prototype binary PROM matrix. The memory switching in a‐Si:H is current induced and requires very low energy (∼1 nanojoule for 1 μm2 area of a 0.2 μm thick film) to switch from ∼1010Ω to ∼105Ω. Resistivity‐tailored, amorphous Ge1−xMx (M=Al, Cu) provides the (synaptic) ballast resistor in the microswitch. A novel side saddle test structure exhibits a potential for a very high density binary connection matrix.

Neural network based feed-forward high density associative memory

A novel thin film approach to neural network based high density associative memory is described. The information is stored locally in a memory matrix of passive, nonvolatile, binary connection elements with a potential to achieve a storage density of 109bits/cm2. Microswitches based on memory switching in thin film hydrogenated amorphous silicon, and alternatively in manganese oxide, have been used as programmable read-only memory (PROM) elements. Low energy switching has been ascertained in both these materials. Fabrication and testing of memory matrix is described. High speed associative recall approaching 107bits/sec and high storage capacity in such a connection matrix memory system is also described.

Electronic Neural Networks For Global Optimization

An electronic neural network with feedback architecture, implemented in analog custom VLSI is described. Its application to problems of global optimization for dynamic assignment is discussed. The convergence properties of the neural network hardware are compared with computer simulation results. The neural networks ability to provide optimal or near optimal solutions within only a few neuron time constants, a speed enhancement of several orders of magnitude over conventional search methods, is demonstrated. The effect of noise on the circuit dynamics and the convergence behavior of the neural network hardware is also examined.